The Button Collector or When does trend predict future values?

So, you know now who to call if YOU loose a bu...

How many buttons will he have on Friday? (Photo credit: Wikipedia)

Guest essay By Kip Hansen

INTRO: Statistical trends never determine future values in a data set. Trends do not and cannot predict future values. If these two statements make you yawn and say “Why would anyone even have to say that? It is self-evident.” then this essay is not for you, you may go do something useful for the next few minutes while others read this. If you had any other reaction, read on. For background, you might want to read this at Andrew Revkin’s NY Times Dot Earth blog.

­­­­­­I have an acquaintance that is a fanatical button collector. He collects buttons at every chance, stores them away, thinks about them every day, reads about buttons and button collecting, spends hours every day sorting his buttons into different little boxes and bins and worries about safeguarding his buttons. Let’s call him simply The Button Collector or BC, for short.

Of course, he doesn’t really collect buttons, he collects dollars, yen, lira, British pounds sterling, escudos, pesos…you get the idea. But he never puts them to any useful purpose, neither really helping himself or helping others, so they might as well just be buttons, so I call him: The Button Collector. BC has millions and millions of buttons – plus 102. For our ease today, we’ll consistently leave off the millions and millions and we’ll say he has just the 102.

On Monday night, at 6 PM, BC counts his buttons and finds he has 102 whole buttons (we will have no half buttons here please); Tuesday night, he counts again: 104 buttons; on Wednesday night, 106. With this information, we can do wonderful statistical-ish things. We can find the average number of buttons over three days (both mean and median). Precisely 104.

We can determine the statistical trend represented by this three-day data set. It is precisely +2 buttons/day. We have no doubts, no error bars, no probabilities (we have 100% certainty for each answer).

How many buttons will there be Friday night, two days later? 

If you have answered with any number or a range of numbers, or even let a number pass through your mind, you are absolutely wrong.

The only correct answer is: We have no idea how many buttons he will have Friday night because we cannot see into the future.

But, you might argue, the trend is precisely, perfectly, scientifically statistically +2 buttons/day and two days pass, therefore there will be 110 buttons. All but the final phrase is correct, the last — “therefore there will be 110 buttons” — is wrong.

We know only the numbers of buttons counted each of the three days – the actual measurements of number of buttons. Our little three point trend is just a graphic report about some measurements. We know also, importantly, the model for the taking the measurements – exactly how we measured — a simple count of whole buttons, as in 1, 2, 3, etc..

We know how the data was arrived at (counted), but we don’t know the process by which buttons appear in or disappear from BC’s collection.

If we want to be able to have any reliable idea about future button counts, we must have a correct and complete model of this particular process of button collecting. It is really little use to us to have a generalized model of button collecting processes because we want a specific prediction about this particular process.

Investigating, by our own observation and close interrogation of BC, we find that my eccentric acquaintance has the following apparent button collecting rules:

  • He collects only whole buttons – no fractional buttons.
  • Odd numbers seem to give him the heebie-jeebies, he only adds or subtracts even numbers of buttons so that he always has an even number in the collection.
  • He never changes the total by more than 10 buttons per day.

These are all fictional rules for our example; of course, the actual details could have been anything. We then work these into a tentative model representing the details of this process.

So now that we have a model of the process; how many buttons will there be when counted on Friday, two days from now?

Our new model still predicts 110, based on trend, but the actual number on Friday was 118.

The truth being: we still didn’t know and couldn’t have known.

What we could know on Wednesday about the value on Friday:

  • We could know the maximum number of buttons – 106 plus ten twice = 126
  • We could know the minimum – 106 minus ten twice = 86
  • We could know all the other possible numbers (all even, all between 86 and 126 somewhere). I won’t bother here, but you can see it is 106+0+0, 106+0+2, 106+0+4, etc..
  • We could know the probability of the answers, some answers being the result of more than one set of choices. (such as 106+0+2 and 106+2+0)
  • We could then go on to figure five day trends, means and medians for each of the possible answers, to a high degree of precision. (We would be hampered by the non-existence of fractional-buttons and the actual set only allowing even numbers, but the trends, means and medians would be statistically precisely correct.)

What we couldn’t know:

  • How many buttons there would actually be on Friday.

Why couldn’t we know this? We couldn’t know because our model – our button collecting model – contains no information whatever about causes. We have modeled the changes, the effects, and some of the rules we could discover. We don’t know why and under what circumstances and motivations the Button Collector adds or subtracts buttons – we don’t really understand the process – BC’s button collecting because we have no data about the causes of the effects we can observe or the rules we can deduce.

And, because we know nothing about causes in our process, our model of the process, being magnificently incomplete, can make no useful predictions whatever from existing measurements.

If we were able to discover the causes effective in the process, and their relative strengths, relationships and conditions, we could improve our model of the process.

Back we go to The Button Collector and under a little stronger persuasion he reveals that he has a secret formula for determining whether or not to add or subtract the numbers of buttons previously observed and a formula for determining this. Armed with this secret formula, which is precise and immutable, we can now adjust our model of this button collecting process.

Testing our new, improved, and finally adjusted model, we run it again, pretending it is Wednesday, and see if it predicts Friday’s value. BINGO! ONLY NOW does it give us an accurate prediction of 118 (the already known actual value) – a perfect prediction of a simple, basic, wholly deterministic (if tricky and secret) process by which my eccentric acquaintance adds and subtracts buttons from his collection.

What can and must we learn from this exercise?

1. No statistical trend, no matter how precisely calculated, regardless of its apparent precision or length, has any effect whatever on future values of a data set – never, never and never. Statistical trends, like the data of which they are created, are effects. They are not causes.

2. Models, not trends, can predict, project, or inform about possible futures, to some sort of accuracy. Models must include all of the causative agents involved which must be modeled correctly for relative effects. It takes a complete, correct and accurate model of a process to reliably predict real world outcomes of that process. Models can and should be tested by their abilities to correctly predict already known values within a data set of the process and then tested again against a real world future. Models also are not themselves causes.

3. Future values of a thing represented by a metric in data set output from a model are caused only by the underlying process being modeled–only the actual process itself is a causative agent and only the actual process determines future real world results.

PS: If you think that this was a silly exercise that didn’t need to be done, you haven’t read the comments section at my essay at Dot Earth. It never hurts to take a quick pass over the basics once in a while.

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222 thoughts on “The Button Collector or When does trend predict future values?

  1. “The only correct answer is: We have no idea how many buttons he will have Friday night because we cannot see into the future.”

    — I would say that states things a little more harshly than is justified and that a more correct answer is “We cannot be sure, but the trend indicates a best guess of…” I forecast using trends all the time. A trend is useful even if it is only partially informative. No, we don’t know the underlying equation and we might be wrong but in many cases it doesn’t matter.

    What’s missing here is the intended use of the forecast. If your boss is asking you to forecast sales, you’d better not tell him that there’s no way to know for sure. Before firing you he’ll say he knows that, but the business needs to make decisions on inventory and capacity. It doesn’t matter if you’re a bit wrong because you’ll simply adjust the forecast. But if your boss is deciding whether to decarbonize the global economy at a cost of several trillion dollars, then the strength of your model had darn well better justify it. Otherwise, the answer is going to be to keep studying and keep him posted on your progress, but hold off on the big spending.

  2. Nicely put, thanks. Yes, too often people are lulled into thinking the pretty graph has meaning beyond being a useful display device.
    Cause is everything, and when it comes to “global warming” there are nowhere near enough causal factors that can be relied upon to produce proof – it is mostly pure speculation, the mind boggles at the use of these graphs to bring in monstrous taxes and trading schemes for miniscule human introduced increases of one of the worlds most important elements for the survival of life on this planet

  3. if the underlying process is regular and invariant, then trend is, indeed, of predictive value with great accuracy.
    for example, i need not specify planetary orbital paths nor gravitational constants to tell you that between now and a year from now there will be @ 365 cycles of day/night.
    but this in no way validates ‘statistics’ as anything remotely scientific. it is a tool of numerologists and no less mystical than tea leaf reading.
    nevertheless, i think it may be worth noting that statistics, like the broken watch, has its moments on occasion – if only to make sure this type of occurrence is properly classified as special and exceptional.

  4. author is getting on a high horse over words. I’ve used trends to predict the future b4. Distinguishing between models and statistical trends is a grey area.

  5. jim2 says:
    October 17, 2013 at 7:26 pm
    He will have between 0 and a googleplex of buttons. I’m 95% certain of it.

    I’m 100% sure he’ll have between 0 and infinity (inclusive).

  6. Based on current trends, how many months until WUWT reaches 2 billion views? I guess this cannot be safely predicted as unforeseen events may change current viewing habits.
    I am astounded, as a dedicated visitor since well before Climategate, at how rapidly the number of views is mounting – around a million a week. Astounded, but not surprised. Congratulations, WUWT!

  7. On the whole you’re right. However. you don’t need to know every cause. In fact, you can never know when you possess a complete set. You only need to know the more common and major ones. If this weren’t true, then statistical mechanics would have zero value.

  8. There are systems for which there are deterministic, physical causes of trends that can be characterized by parameters whose values are determined by fitting noisy data trends. Future average performance is projected by extrapolating the trend via its parameters. IPCC climate modelers would have us believe that future climate really does depend deterministically on CO2 levels. The obvious failure of their models to predict the current arrest of global temperature anomalies says that they have failed to account for some important physical variables.

  9. It’s funny and sad that we have to remind scientists (and laymen) how to do statistics using pre-school techniques. Well done though, if the articles was shorter, perhaps science journalists would read it too (and not just the abstract).

  10. Two weeks on, the number of buttons was found to have been constant for 17 days. I understand that this matter was then considered by the IPCC (Intergovernmental Panel on Clasps and Closures) and they reached a consensus opinion that, as 10 of the highest observations of number of buttons were made in the previous fortnight, the collection of buttons was clearly continuing to grow.

  11. “…he collects dollars, yen, lira, British pounds sterling, escudos, pesos(…)sic you get the idea. But he never puts them to any useful purpose, neither really helping himself or helping others…”

    Sorry but the author is incorrect. Savings==investment. If the saver has chosen not to benefit then the benefit goes to somebody else. A few years ago wealth was transferred from some savers, whose saving were in US dollars, to savers whose savings were in bank shares. About a trillion dollars was stolen from one class of saver and handed to another. Saving always benefits SOMEBODY.

    It is not possible to “neither help oneself nor others” by saving. Saving is always put to useful purpose.

    The foregoing assumes that the saving are “real”, i.e. more was produced than consumed. Fake savings can be produced by favoured classes by permitting them to write up the value of imaginary assets(that’s what the banker thieves did. Oh and are still doing by the way(QE x)).

  12. So, just because there has been a pause in rise of global mean surface temperature does not mean we can reject the physics of global warming? Rats.

  13. The trend just provides a first approximation for a model. The Pearson statistic may help you decide how much you want to bet on a linear extrapolation. It doesn’t take into consideration the consequences of being wrong. I’m betting I’ll wake up tomorrow, and that I’ll keep waking up each day for some time. Happily, if I’m wrong, I’ll never know. However, there would be consequences for others I’d rather they didn’t have. There is quite a bit of money on the line for insurance, and underwriting is a serious business. Back to trends again. Dealing with relatively large numbers of people helps. Do we have enough people for really good statistics (see DAV above)? We still don’t have our Harry Seldon, nor psychohistory.

  14. I believe that Nassim Taleb refers to this as the “falacy of the turkey” in both the Black Swan and Antifragile. Then again, what does he know…being a independently wealthy Wall Street “pit trader”, who AFTER achieving that success, went back to graduate school and earned his academic credentials. Let’s hope he’s no turkey!

  15. Trends which are stable despite a variety of potential disruptive events over the observed period will induce some confidence that it will continue. Trends which wobble in response to interventions may give some indication of causality. Climate science has neither.

  16. Well done!

    Yes, as others have pointed out, we need to make guesses about the future and sometimes those guesses turn out to be accurate (the IPCC is merely precise, lol), but, in the end, all we have based on statistics which only describe the past or present is:

    a guess.

  17. It was interesting and informative to read the comments at Dot Earth Kip Hansen. Even relatively intelligent posters have difficulty understanding this simple concept that trend of the past is not necessarily predictive of the future especially at longer time scales. It is clear that many do not understand the complexity of the solar, ocean, atmosphere coupled dynamics. It was especially interesting that your link to Marcia Wyatt’s new paper was ignored. Much has been ignored regarding the oceans while focusing on the atmosphere over the last twenty years.

  18. Kip Hansen: “The only correct answer is: We have no idea how many buttons he will have Friday night because we cannot see into the future.”

    The correct answer is that you’ve made a mash of the whole topic. It’s certainly true that you cannot guarantee now the then that has yet to happen. But statistics are not and have never been a guarantee.

    Nor is there any necessity, or even sanity, in attempting at self-fulfilling logical positivist approaches to causality. Newton went commando with Hypothesis non Fingo. Kepler was a pure data fiddler. So was Galileo. Point of fact, any postulated cause has less expectation for correctness, in general, than a long chain of validated predictions arising from a non-causal model. And that’s putting aside any expected issues that arise from the field of physics and it’s distinct reliance on impossible objects taken to every infinite asymptote they can find.

    And yet the things still work out. The problem here isn’t statistics, nor trends generally, but one of repetition and validation of long chain of predictions. Especially when you have non-replicable observables and need to see your trend go both up and down. The problem isn’t the math. And it isn’t the models per se.

    It’s a complete disregard for validating the results before launching into religious pronouncements that the world is doomed, that CO2 *is* the climate, or that there must be something terribly wrong with math.

  19. Statistics are strange things. Some thoughts:

    Lord Rutherford is supposed to have said: “If your result needs a statistician then you should design a better experiment.”

    90% of lung cancer victims have smoked. 100% of them have drunk water.

    Odds of a million to one are considered remote. However, I can do something that has odds of, approximately 80,658,175,170,943,878,571,660,636,856,403,766,
    975,289,505,440,883,277,824,000,000,000,000 to 1 against any time you like. I just need to put a deck of cards in a line.

  20. I liken it to a person walking up a hill and down the other side, and measuring their altitude at every step. Even if we know exactly what the length of their stride is, we cannot predict from the past data what their altitude will be on the next stride unless we know exactly what the shape of the hill is. In fact, if all we have is the data from the first 25% of the journey, the data would provide a trend suggesting the person is headed for outer space.

    Interestingly, if we stopped our data collection when the person was 5 steps past the crest of the hill and on their way back down, we could say, and correctly so, that the person’s last 10 steps were the highest in the entire record of the journey. It would not change the fact that the person is going down hill.

  21. It is amazing how many commenters still, even after reading the article, cling to the idea of a predictive trend.

    A trend never predicts future values. By definition, as the author pointed out, a trend[line] is fit to the data (not the other way around).

    Some commenters seem to be unable to uncouple the trend from the process beneath.

    Measurements Monday, Tuesday, Wednesday, and Thursday, of empty milk bottles on the front porch being, 1,2,3, and 4 respectively create a trend that predicts 5 empty milk bottles on Friday.

    Unfortunately for the trend based prediction user, there are only 4 quart bottles of milk, and the milkman brings new milk on Fridays, so the correct answer is zero.

    The trend is utterly useless as a predictor — always.

  22. This is what worries me about share traders who use charts and “technical analysis” as predictions. Expressing the price as trying to break resistance lines and all that.
    Prediction, it ain’t!

  23. But half the articles on this site back predictions based on trends, usually of the form “it is going to get colder”. Are you saying all these articles are worthless?

  24. Young Antounie Caen’s schoolteacher-
    Swans are white, always have been white and always will be young Antounie, so get that through your thick head and pay attention in class meboy.

    http://www.svswans.com/black.html

    @gnomish-
    “if the underlying process is regular and invariant, then trend is, indeed, of predictive value with great accuracy.
    for example, i need not specify planetary orbital paths nor gravitational constants to tell you that between now and a year from now there will be @ 365 cycles of day/night.”

    Black swan event and poof! Just that we are lulled into thinking that it will be so from trend but should we ever have reason to believe otherwise then a helluva lot of other theories and science will evaporate instantly.

  25. “The trend is utterly useless as a predictor — always.”
    This is absolutely incorrect. In the absence of any other data, past trends are the BEST predictor of future values.

    This entire article shows a complete failure to understand statistics. Statistics can be used to make predictions, based on certain assumptions. If those assumptions are correct, the prediction is MORE LIKELY to be correct. If the assumptions are wrong, then the prediction is LESS LIKELY to be correct. No where are guarantees made.
    The correct prediction in the article would have been “Given the assumptions we are making (iid, normal, etc), we predict that it is most likely that there will be 110 buttons, give or take (a very large number).” No statistician would EVER say “There WILL BE 110 buttons.” Because statisticians actually understand statistics.

    “We can’t know the future” is an utterly silly argument. Will the sun rise in the East tomorrow? I hope you didn’t say “yes”, because you can’t know the future! If I let go of this pencil I’m holding in the air, will it fall to the ground? I hope you didn’t say “yes”, because you can’t know the future!

    Bah. The only thing this article shows is how many supposedly educated people can utterly fail to understand simple concepts. Statistical predictions are not Truth, they are well formed Guesses. And guesses can be wrong. Anyone, like the author, that fails to understand this should stay well away from statistics.

  26. This analysis fails to mention response times when you are dealing with a time series. If you consider response times, you are left with a strange result, that although a trend cannot be used to predict a future term, it can estimate unpredictable future terms with various degrees of lack of confidence.
    An earlier blogger has used the case of planetary orbits. These have a long response times in regard to position relative to a datum. If I am given a graph of daily positions of a planet, I can estimate a position in the next day to a high degree of accuracy.
    If, however, I am counting buttons, which my wife can throw away in a second, then I would not try to estimate ahead. The response time can be very short, like her temper can.

  27. @Fred the Statistician-
    “This entire article shows a complete failure to understand statistics. Statistics can be used to make predictions, based on certain assumptions. If those assumptions are correct, the prediction is MORE LIKELY to be correct. If the assumptions are wrong, then the prediction is LESS LIKELY to be correct. No where are guarantees made.

    That’s what the article points out- ie the bleeding obvious and it’s just reminding us all about the implicit assumptions underlying the sun rising tomorrow or the pencil dropping. We answer ‘yes’ and tend to ignore the qualifying assumptions underlying it.

  28. Mark XR says:
    October 17, 2013 at 8:23 pm
    So, just because there has been a pause in rise of global mean surface temperature does not mean we can reject the physics of global warming? Rats.

    If you mean, post modern IPCC physics, that CO2 is the key driver of modern climate change, yes we can. Historical evidence has all but debunked the correlation between global temperature and CO2.

  29. Steve M. from TN says:

    October 17, 2013 at 7:43 pm

    jim2 says:
    October 17, 2013 at 7:26 pm
    He will have between 0 and a googleplex of buttons. I’m 95% certain of it.

    I’m 100% sure he’ll have between 0 and infinity (inclusive).

    Not exactly. He will have between 0 and a countable infinite number of buttons, all terms of the infinite series being positive even numbers (unless he goes “broke” and winds up with zero). He will not have any odd number of buttons.

  30. You are correct – stats are only as good the information entered. And as always there are as many economists predicting the future as there are economists. One may as well watch Star Trek. No matter what anyone says here – whatever their forward programing it is a guess based on a past that may or may not be anywhere near accurate. Regards to inventory – how often shelves are without a product is common. If climate is an indication then it is no wonder why models are next to useless. What we do know is there are ice ages and interglacial periods. The rest our present understanding is next to nil – like the weather.

  31. What ever happened to ‘The trend is your friend’?
    Certain kinds of trading rely heavily on this principle.

  32. How do you know that BC is reporting the number of buttons accurately? What if his buttonometer hasn’t been calibrated. What if he is in an Urban Button Island. Maybe he adjusted the count to conform to the Summary for Button Makers.

    Heh – there haven’t been any buttons for 17 years.

  33. Gary Larson’s The Far Side’s 4 personality types.
    1. The glass is half empty.
    2. The glass is half full.
    3. Half empty!..no wait.. Half full.!….no wait
    4. Hey I ordered a cheeseburger.!

    1. The buttons are decreasing.
    2. The buttons are increasing
    3. We don’t know whether they are increasing or decreasing, or staying the same.
    4. Unless we reduce our button consumption, things will get much worse than we thought, and the world will end as know it.

  34. In the UK, I think the few minutes spent reading this article would be of great benefit both to Ed Davey (if he is capable of understanding it) and Sir Mark Walport, as well as several members of Parliament’s Energy and Climate Change Committee.

    “Back to basics” on the use of statistics, with a reminder of the number of potential interconnected variables that might contribute to a model of global climate, should get them to pause for thought. Clearly this will not happen, because we are dealing with a religious type of belief (which history shows us is all about the political power to soak the poor), not statistics or science.

  35. If I could be bothered reading all this, I think I would disagree.
    Sure, the trend is a product of the data, but if the data is measuring something which has inertia we can expect data to continue on trend. Given that global weather has inertia one can expect trend to continue. If there is a sixty year cycle that is obvious over one hundred and fifty years of data one would expect a certain amount of time to pass before that cycle decays and a new cycle / trend is established. There is a long trend out of the Little Ice Age. That will not just reverse. The value of the trend is that it enables you to see when the data is starting to stray off the trend. This is something the IPCC never worked out. They were silly enough to draw a straight line through a cherry-picked bit of the data and call it significant when the bigger picture screamed something else. Their model is broken and they just dont like the reality imposed by the real data (trend).

    If you think this is wrong – consider investors in stock markets whose whole living is dependent on picking trends in data and then working out when the data has gone off trend in a significant way. Like climate researchers and climate , each investor does not need to know the detailed internal workings of a company to be able to see when the data is straying off trend and a buy/sell decision be made. I dont need to know anything about the science of climate change to see its drifted off the IPCCs trend and the whole AGW/CO2 business is now a SELL option. There was an excellent post just the other day on this very topic.

    To say that you can never predict the future based on a trend leaves us saying we can never be sure the sun will come up in the morning. This may be true but it is an excellent working hypothesis.

  36. Past performance etc does not guarantee future returns, is a caution given to any small investor in the UK. However past behaviours do indicated the likelihood of future behaviours. When working in forensic mental health we used various tools to assess the risks patients with mental health problems who were also offenders posed to society. These assessments were in the main pretty useful. These type of assessments can also extend to how a person will react to a given piece of research. If they have always rejected a certain stance in the past, they are likely to do so again whatever the quality of the study. This underpins my belief that there is one heck of a lot more subjectivity in Climate science than we recognise, and a stance that makes me unpopular with both the consensus and skeptic side, many of whom like the reassurance of definite indisputable facts. From a personal perspective I see two general trends in opinions, but millions of different specific views on the issue, a number usually correlated to the amount of people who are asked.

  37. About trends –
    Yes, the trend is indeed your friend.
    But wise stockbrokers know that a trend continues until it doesn’t.

    And, @ Observa
    Here in Australia all our swans are black.

  38. Definition of Trend:

    1. The general direction in which something tends to move.
    2. A general tendency or inclination.
    3. Current style.

  39. observa says:

    October 17, 2013 at 10:38 pm

    Young Antounie Caen’s schoolteacher-
    Swans are white, always have been white and always will be young Antounie, so get that through your thick head and pay attention in class meboy.

    He forgot to add “The science is settled”!

  40. If you think this is wrong – consider investors in stock markets whose whole living is dependent on picking trends in data and then working out when the data has gone off trend in a significant way. Like climate researchers and climate , each investor does not need to know the detailed internal workings of a company to be able to see when the data is straying off trend and a buy/sell decision be made. I dont need to know anything about the science of climate change to see its drifted off the IPCCs trend and the whole AGW/CO2 business is now a SELL option. There was an excellent post just the other day on this very topic.

  41. In my humble opinion, whenever anyone compares any scientific statistical argument to the stock market, they’ve lost the plot.

    When I take a hot caserole pot from the oven, I use oven gloves so that I don’t burn myself. An hour later, then I take the same caserole pot from the table to sink, I don’t use oven gloves. I have successfully predicted the future. Sure, I can’t know the exact temperature, but I can be pretty sure it will be close to room temperature. I can even take a few temperature readings soon after removing it from the oven, and make a pretty good guess at when it will be comfortable to hold the pot.

    Note that unlike the stop market, this is unlikely to be affected news of some CEO having an affair, or by whether the Republicans and the Democrats agree on some particluar financial arrangement. Unlike the stock market, the pot is unlikely to be impacted by whether some trader had a headache this morning. Comparing the two is stupid

    In fact, the author of this posting completely agrees. He says:

    Armed with this secret formula, which is precise and immutable, we can now adjust our model of this button collecting process.

    And here we get to the climatology comparisons. Some climate scientists seem to believe that they know the secret formula – in fact, they go further and claim it isn’t secret, but common knowledge based on physical principles – and using this formula they can predict the future using computer-based models. Other scientists, and much of the WUWT readership, believe that the formula is too simplistic, that it doesn’t allow for a variety of internal and external influences, or that it is tuned incorrectly so that some real influences (such as CO2 forcing) are assigned too much of an influence.

    But it isn’t the stock market. And it isn’t some random collection of buttons. So don’t make a silly comparison to ‘prove’ a point.

  42. In the example given, by analysing all past data, you would be able to make a prediction to some level of confidence (say 90%) what the minimum & maximum number of buttons the next day was likely to be. This is done all the time in many fields – for example we don’t know how many particles a radioactive source will emit in the next minute, and we don’t know the underlying mechanism which causes a particular atom to split, but we can estimate a range of values and we can use that knowledge for useful purposes (in a nuclear power station for example). To characterise this nuanced outcome as “We have no idea how many buttons he will have Friday night because we cannot see into the future” is absurd and just plain wrong – we have an “idea” but we don’t know for certain.

  43. Suppose that a car at a distance of 500 meters starts driving in your direction and for 490 meters it drives in a straight line at constant velocity. The car has no driver. It has been programmed. You don’t know the program. You know nothing about the causes that make the car drive in a straight line for about 490 meters. What would you do?

  44. On statistics, it is estimated that 50% of all people who have ever lived on earth are alive today. Does this mean I only have a 50% chance of dying? ;-)

    Of course this post is overly simplified, it’s loosely applying the ad absurdum rhetorical tool. I’m sure most folk frequenting this particular virtual space are perfectly aware that in practice we can attach PROBABILITIES to certain potential outcomes based on HISTORICAL trends derived from hard data.

    Nothing wrong with that, and many people base important decisions (investment, policy, research, travel, etc.) on that very concept. In fact we all do this constantly. It’s an integral part of human existence. From the moment we wake up of a morning, we expend a great deal of mental energy throughout the day subjectively assessing risk on the basis of the cognitive models we have constructed over time in accordance with the lessons life has taught us (just take the drive to work, for instance). It’s part of the subjective cost/benefit analyses that life demands of us all continually, and we are all far more adept at it than many people realize. (Indeed, the better we are at this, the more Antifragile we become.)

    The point is, there is a huge difference between modeling probable outcomes of physically quantifiable processes, and modeling trends arising from human action. The former entails a higher objectivity quotient, the latter a great deal more subjectivity, thus making it less predictable based on existing trends. And I think that that is an important distinction to make in this thread. (I call this distinction the Light/Life Ratio.)

    However, both types of forecasting are at all times subject to Taleb’s now proverbial Black Swan Event (meteor impact, unforeseen geoplitical event, etc.). Obviously, therefore, even the highest probabilities properly assinged to a given trendline can miss the mark completely. And it’s this fact that I think Kip Hansen is getting at here when he asserts we cannot predict the future using past events.

    I imagine that the above is as plain as a pikestaff to most folk here, but I suspect that there are a great many others for whom this is not so obvious. And it’s their hearts and minds we seek to win from the prophets of the Great and Terrible Day of Global Thermageddon!

    Just remember, “He is no friend that guides not, but ridicules, when one speak from ignorance”.

  45. I would add a caution to the author’s conclusions. No matter how perfectly one understands the underlying causes of a phenomenon, models will still utterly fail if the phenomenon being modelled is inherently nonlinear. As a simple example, consider orbital mechanics. We understand Newtonian mechanics (as modified by relativity) with sufficient precision to be able to predict the ground paths of solar eclipses, the trajectory of Voyager or Cassini, or the return of Haley’s Comet. These are linear systems and thus can be modelled with some predictive accuracy. However, a nonlinear system (like weather) cannot be modelled over any significant duration because it is sensitive to infinitesimal changes in starting conditions. Nonlinearity prevents prediction over anything beyond the shortest timelines. Lorenz recognized this in the 60s, and the IPCC acknowledged it decades ago (and then promptly swept scientific reality under the carpet and spent the next several years desperately clinging to a politically approved but empirically falsified hypothetical linear relationship between anthropogenic CO2 emissions and delta T).

  46. yes, observa- a single contradiction falsifies a logical proposition.
    on the other hand, it is the nature of an identity that it is never, in the specified context, falsified – ever.
    those are the basics of epistemology and the nature of knowledge.
    but the law of gravity is not a black swan.
    tomorrow will come whether anybody believes it or not.
    and that will not be falsified.

  47. This issue was treated once very well by Karl Popper, e.g. in The poverty of historicism. Is has nothing to do with statistics as it can be summarized by his statement that regularities or not laws of nature. Even the sun-rise tomorrow is not a law of nature and cannot be predicted. We do not even know the probability of this event.

  48. gnomish says:
    October 18, 2013 at 2:48 am

    tomorrow will come whether anybody believes it or not.
    and that will not be falsified.
    ———————————–
    There is only the eternal now, the same as yesterday, today, and tomorrow, which ;illusionary; concepts are only relative perspectives of the mortal mind which observes the apparent kaleidoscopic environment presented to its inherent senses.

  49. Wonderfully put, a clear & simple explanation of statistical modelling!

    My Higher National Certificate maths teacher, one Ms Mallik, who trained as an engineer, said interpolate with some confidence, extrapolate at our peril!!!! ;-)

  50. MHX says:
    Suppose that a car at a distance of 500 meters starts driving in your direction and for 490 meters it drives in a straight line at constant velocity. The car has no driver. It has been programmed. You don’t know the program. You know nothing about the causes that make the car drive in a straight line for about 490 meters. What would you do?

    well if the car had been going left and right and then went off in another direction for 17 meters AND if I moved I would have to drop and break pretty much everything I had earned in the last 10 years then maybe I would wait and see what happens…

  51. Hi, Kip. I’m afraid I agree with Fred (the Statistician) above, and others who have made similar comments. I think you have gone quite overboard by saying we have “no idea” what will happen next. This is the sort of black and white thinking that gets a lot of people into rhetorical trouble. Statistics is not about black and white – it is about quantifying the grey.

    On a related point, you said “Models, not trends, can predict, project, or inform about possible futures, to some sort of accuracy.” This is true, about models, but it is (of course!) also true about trends. You then add, “Models must include all of the causative agents involved which must be modeled correctly for relative effects.” This is absolutely incorrect.

    Models *never* include “all of the causative agents involved.” Never. A model is a simplification of the real world. By definition, some things are left out. We hope, when building our model, that only trivial causes (or trends) are left out, and what we have is a pretty good approximation. But models are always a simplification of the real world.

    Further, it is entirely reasonable to sometimes leave out ALL causative agents. This would be an empirical model – i.e., a model based only on data, and one that tries to capture or describe the trends found in our data set. Absent any other knowledge, an empirical model will be the best predictor of future values – i.e., better than random guessing (which – absent any other knowledge – would be our only other option.)

    Finally I would submit that empirical modeling is always the first step of any attempt to understand a phenomenon. That is, it is the first step in the scientific process. You first notice a pattern in nature or society. You try to describe that pattern, and – if it persists to some degree – you then investigate the pattern to look for causal agents – the underlying mechanisms that produce the pattern. But you have to notice the pattern first, and “noticing a pattern” is another way of saying “building an empirical model.”

    The Theory of Gravity is the classic example of this. The empirical results are so thoroughly robust and understood that we even call it a Law. But we don’t know what “causes” gravity … we can model it very, very well, but we don’t know the cause. (Yes, there are a few hypotheses of late; but these are as yet uncertain.)

    Cheers.

  52. Jan Smit says:
    October 18, 2013 at 2:09 am

    “On statistics, it is estimated that 50% of all people who have ever lived on earth are alive today.”

    Please don’t make up such rubbish, as you will give WUWT a name for inaccuracy. The estimate is wildly wrong. Try circa 7%.

    Why do I always have to be “the bad guy” and correct rubbish on this site? Note that I correct probably less than 1% of it. Probably much less than .1%.

  53. And in case you northern hemispheric folk were wondering about that record minimum for Canberra (we’ve only had a reasonably comprehensive Stevenson Screen system since 1910 recall), the bushfires around Sydney you’re witnessing on the news are occurring only 240km ( 149 miles) from Canberra.
    Yes folks it’s strong hot northerly winds in Sydney, coupled with high fuel loads after a couple of wet years and all those people who like living among the gum trees rather than tar and cement.

  54. The stocks numbers are just numbers that depend an infinite number of complexly related variables – but in the end they are just data.

    Temperature data is just numbers that depend on a infinite number of complexly related variables – but in the end it is just data.

    They are both just data sets that can be looked at just like data sets. There is nothing sacrosanct about temperature data that means its off limits to speculation

    Climate though has more inertia – that’s why its took 15 years for anyone to notice that the data was definitively heading off the IPCC’s trend. And why it will take another 15 years before anyone can agree what happens next. Sticking to Akasofu’s trend and that’s good enough for me for the short term until I can see a deviation away from that. Then we re-think.

    The point here is if we all sit here and just say there are no such thing as trends you effectively are saying that the future is unknowable which doesn’t help anyone. It effectively leaves us with the IPCCs model lines out to 2100 as the only people with skin in the game.
    Whats science all about if it isnt putting up a speculative hypothesis and seeing how it works out – there is no value in waiting until the end of the universe and saying Oh, so that what happened!

  55. If every event is causally related to every other event, past or present, forming a compete model is a forbiddingly large enterprise. Worse if the causal relations are non-linear. In brief – trends and models are just masks to conceal our ignorance. However all is not lost – we can and do form very accurate predictions – so accurate that they are ALWAYS correct.

    If I am x% (it does not matter what value ‘x’ has – this does not affect the accuracy of our prediction) certain that the sun will or will not rise tomorrow. So long as I do not claim perfect knowledge or absolute certainty, I shall always produce correct predictions; even better, I can never produce wrong ones!

    This is why IPCC and all the other quacks playing with models/ and trends to produce forecasts/predictions can continue to syphon money from tax payers – they are never wrong! Even if their predictions never correspond to the events, they can (and do) claim increased confidence in the skill of their models and they can justify whatever causal relations they may fancy – yes – even that Great Greenhouse in the Sky!

  56. @ Ted

    We do actually have no idea what will happen next based on the trend. The trend is simply a statistical analysis of the data set. It includes no context, simply an equation that is defined by the data. —- It contains nothing useful with respect to predicting the next measurement – zip, zero, nada.

    Models, OTOH, are sometimes useful predictors of future behavior, measurements, values, or occurances, depending upon the fidelity by which they capture/represent the phenomena they are modeling.

    All too often — even after a correct explanation, people CONFLATE the predictive ability of models and trends.

  57. @acementhead

    Point taken, sorry for being so sloppy! However, it was only meant as joke about how statistics can be used to assert any nonsense (hence the winking smiley). But I consider myself suitably chastised…

    Here’s a good link to support acementhead’s correction of my spurious nonsense:

    http://www.prb.org/Publications/Articles/2002/HowManyPeopleHaveEverLivedonEarth.aspx

    Do you agree though, acementhead, that statistics can and are used to support all manner of claptrap?

  58. mhx says:
    October 18, 2013 at 2:06 am

    “Suppose that a car at a distance of 500 meters starts driving in your direction and for 490 meters it drives in a straight line at constant velocity. What would you do? “
    —————

    I would predict that the right-front tire would “blow-out” at the 492 meter mark and the car would veer off the road and crash. That is unless my earlier prediction proved to be correct that it would run out of fuel at the 491 meter mark. :)

    Predicting earth’s climate for the next 100 years is akin to predicting Super Bowl or NBA winners for the next 10 years. Iffen you think you can, …. go for it, ……. place your “bets” today.

    If one could magically remove all of the CO2 from the atmosphere there would be no measurable effect/change in the near-surface air temperatures and that is because 398 molecules of CO2 are irrelevant when intermixed with 20,000 to 40,000 molecules of H2O vapor.

    Thermal energy in the near-surface atmosphere is NOT cumulative from one (1) week to the next and surely not one (1) year to the next.

  59. LevelGaze says:
    October 18, 2013 at 12:41 am

    About trends –
    Yes, the trend is indeed your friend.
    But wise stockbrokers know that a trend continues until it doesn’t.

    “The trend is your friend until the bend in the end.”

  60. I think people are confusing likelihood’s with certainties. Trends give a reasonable indication of a likelihood when used in conjunction with other considerations, 100% certainties are more related to prophecy. My fellow believers in the luke-warm camp have made this error on many occasions by looking at trends in isolation and making predictions which have failed to materialise.

  61. Jan Smit and acementhead

    Jan the link says – “And semi-scientific it must be, because there are, of course, absolutely no demographic data available for 99 percent of the span of the human stay on Earth. Still, with some speculation concerning prehistoric populations, we can at least approach a guesstimate of this elusive number.”

    Ace asserts you are wrong; in which he is almost certainly right and then he provides another “correct” estimate, in which he is almost certainly wrong. A range of somewhere between your two percentages is a better guess. But it is o so wide and board. The “some speculation” is like “total speculation” and so the answer is totally speculative.

    And now the question: What do past population trends tell us about future population trends? Nobody sixty years ago would have been able to predict current population trends from past population trends.

    Kips point is very well taken and very humbling, “We know nothing about the future” “We know damned little about probabilities” And yet.. what else do we have?

    As the pilot observed: “The runway is always long enough, until it isn’t”

    And as Oliver Cromwell said, “Who can love to walk in the dark? Yet Providence doth so often dispose”

  62. @Karl – thanks for the reply. You said, “We do actually have no idea what will happen next based on the trend.” It is the “no idea” part that I object to. That’s what I meant about it not being black or white: ‘know’ vs. ‘don’t know’ is the wrong way to look at it. The trend is useful information even though it is not perfect information. Saying “no idea” is akin to saying the trend gives no information at all, and I would say that is wrong. Cheers.

  63. @ William Abbott

    Yes, William, of course you are right. But I was trying to tie off a potential off-topic discussion started inadvertently by myself due to my own laxity. Accepting I had been sloppy and acknowledging that an acceptable guesstimate might be closer to acementhead’s 7% was my way of willingly giving ground on matters of little consequence so we could all focus on more important aspects of this discussion.

    Though I’m sure we could all have a jolly interesting and constructive discussion on estimates of earth’s past population, this is neither the time nor the place…

  64. ” Fred the Statistician says:
    October 17, 2013 at 10:38 pm

    “The trend is utterly useless as a predictor — always.”
    This is absolutely incorrect. In the absence of any other data, past trends are the BEST predictor of future values.

    This entire article shows a complete failure to understand statistics. Statistics can be used to make predictions, based on certain assumptions. If those assumptions are correct, the prediction is MORE LIKELY to be correct. If the assumptions are wrong, then the prediction is LESS LIKELY to be correct. No where are guarantees made.
    The correct prediction in the article would have been “Given the assumptions we are making (iid, normal, etc), we predict that it is most likely that there will be 110 buttons, give or take (a very large number).” No statistician would EVER say “There WILL BE 110 buttons.” Because statisticians actually understand statistics.”
    __________________________________________________________________________
    And don’t Climate Alarmist Scientists say there WILL be catastrophe? And do statisticians actually understand statistics? I doubt it. Some statisticians understand some statistics and that would be as far as I would go.

    “A Crooks says:
    October 18, 2013 at 12:30 am

    If you think this is wrong – consider investors in stock markets whose whole living is dependent on picking trends in data and then working out when the data has gone off trend in a significant way. Like climate researchers and climate , each investor does not need to know the detailed internal workings of a company to be able to see when the data is straying off trend and a buy/sell decision be made. I dont need to know anything about the science of climate change to see its drifted off the IPCCs trend and the whole AGW/CO2 business is now a SELL option. There was an excellent post just the other day on this very topic.”
    ___________________________________________________________________________
    But are you a stock investor? Do you have any idea how many 100’s of different types of charts there are and you are trying to line up a trend? I personally used about 5 different stats charts and still would get it wrong about 20% of the time. These days the really big boys use their computers and very fancy algorithms to fire out buy and sell orders but the basics are still the different statistical methods.

  65. It was an interesting read. However, if the intent was to demonstrate the foolishness of using ‘statistical’ trends in climate, then the allegory button collecting from Monday to Wednesday what is Friday, was about using trends in weather. The allegory should have been: “I know the button numbers back from June to October, what will will the button count be in October in 10 years time?
    This would have stopped all the comments from people saying they use short term trends all the time – as weather forecasters can – although they can still get it wrong. Once outside a temporal comfort zone though things are different, unless you are a climate ‘scientist’.

  66. Having read both the DotEarth page (and its resident commenters are something else…) and the comments here, I’m somewhat bemused by the number of people who do not understand Kip’s simple message. I’m particularly intrigued by the apparent correlation between those with advanced knowledge and skills, and their lack of understanding of what Kip has written. What I do read in many posts, are rationalizations for beliefs that are based on the statistical analysis of past events.
    Probabilities are not certainties. Statistical analysis allows us to take past facts and develop beliefs about future facts, but it never allows us to actually know the facts – that only happens when the future is past, and we add the future facts to the dataset.
    Knowledge is always based on history. Knowledge of the future doesn’t exist. A belief about some aspect of the future is all there is. Statistical analysis and modelling provide us with degrees of comfort that our beliefs are, or are not, likely to happen, but the uncertainty is never completely quantifiable. It’s improved by the causalities we know and understand, and therefore our beliefs have a higher degree of comfort, and mathematically a higher probability of certainty, but they are never certain. Even the probability of tomorrow is not a certainty, however likely the probability is.
    I note that in many comments on both sites, that individuals with high skills appear to have a stronger belief that their future knowledge is more certain. That’s a rationalization for their beliefs abut the future.
    In the climate science realm, as I suspect in many sciences today, the faith in these models and tools has allowed beliefs to take precedence over facts, the implied point of Kip’s argument. As a consequence, we have predictions (beliefs) presented as certainties. and some of the most convinced that these beliefs are certainties are scientists, the group most expected to understand they are not.

  67. “Models, not trends, can predict, project, or inform about possible futures, to some sort of accuracy. Models must include all of the causative agents involved which must be modeled correctly for relative effects. It takes a complete, correct and accurate model of a process to reliably predict real world outcomes of that process. Models can and should be tested by their abilities to correctly predict already known values within a data set of the process and then tested again against a real world future. Models also are not themselves causes.”

    Let’s say that a statistician is given a dataset containing the length of the day on earth for the last 1000 years. He is not told what the numbers mean (i.e. they are just numbers) or how they relate to real world processes. A competent statistician will easily be able to reliably predict the next number in the sequence.

  68. Key, to me, is this:

    “Models must include all of the causative agents involved which must be modeled correctly for relative effects. It takes a complete, correct and accurate model of a process to reliably predict real world outcomes of that process.”

    Regarding climate models, they must not be including all of the causative agents or their relative effects.

    So, whether they are giving “predictions” or “projections”, they will be wrong.

  69. If, two days later, the number of buttons is the same, or has even dropped by 1, one can always claim that the buttons which “should” be there are instead somewhere in the basement, under the floor. Problem solved.

  70. Its hardly off topic. It’s a perfect example of Kip Hansen’sexcellent analogy and what I want to call, “Cromwell’s Dilemma” Cromwell wanted the future revealed to him through the agencies of divine providence, but concluded the information’s not available to the mortal man. We want the models, the trends, the statistics, to reveal the future to us —- but they don’t. They can’t. They speak of probabilities and probabilities tell us nothing of the future. We are left making decisions about the future, in the dark.

    I know the population statistic was introduced as a humorous lead, but the percentage you used is taken as approximately knowable by all and it isn’t – and population trends happen to be a great example of how unpredictive trends are. Trends are predictive until they aren’t. So they tell us nothing and we have to “walk in the dark”

  71. Paul Coppin says:
    October 18, 2013 at 5:27 am
    “Knowledge is always based on history. Knowledge of the future doesn’t exist.”

    Ya well, the number of buttons of the collector, just like global average temperature, is brown noise, because there is an integrating element (the existing collection / thermal inertia resp.).

    That gives us a probability distribution for the result on day today+n.

    Mark XR says:
    October 17, 2013 at 8:23 pm
    “So, just because there has been a pause in rise of global mean surface temperature does not mean we can reject the physics of global warming? Rats.”

    The opposite is true. Because the Null hypothesis (that warming out of the LIA proceeds as it always has) still suffices to explain everything, we can apply Occam’s razor and discard the CO2AGW theory as unnecessary, it does not add explanative power, more likely, it WORSENs our ability to forecast.

    So you warmists, predict something that we would not have been able to predict with the Null hypothesis and then you MIGHT have something. Refute the Null hypothesis. For now you have NOTHING.

  72. @ William Abbott

    Please, William, I appreciate what you are saying and find myself completely in agreement with it. I regret using the 50% statistic as it’s based on little more than hearsay and has now exercised our minds too much already. Given its total lack of weight for either historical or predictive purposes, I put no store whatsoever in such a figure – it was just an off-the-cuff remark to illustrate a different point. I am perfectly aware that there is no way on God’s earth we can be anything but very uncertain how many people have ever lived. Even today’s population estimates are said by some to be widely off the mark. I thought the phrase “… it is estimated …” would make it nuanced enough to prevent such feedback, but I misjudged it. My bad.

    Can we leave it at that?

  73. The author plays with words . A trend can be a model.
    Trends might indeed show the future and might indeed predict future values. The most common situation is when the trend itself affects people behavior.
    We can also discuss mob or pack behavior, and “If you tell a lie big enough and keep repeating it, people will eventually come to believe it”.
    After a string of defeats i can predict that a certain trainer will not be at front of that team for much longer. Will be this true in all cases, no.
    Can i predict the increasing odds of a divorce based on increasing discussions between a couple? Yes i can.
    Or if a new technology appears,even if i don’t know anything about it but many adopt it then it might show a trend which mean that there are odds that the current increasing trend is repeated in the future.
    Of course using just this kind of prediction is typically of inferior quality in most cases, but it has its value and sometimes it is the only or less worse solution.
    Quantity has a value of its own.

  74. Something else that can affect statistical predictions are the boundary conditions. Does the Button Collector have unlimited storage for his button collection? Does the Button Collector have an unlimited selection of buttons available to be added to the collection? Or, does storage and/or selection have the possibility of changing over time?

    Worse yet, is there anyone who double-checks the actual numbers of buttons (quality assurance) that are in the collection?

  75. The thesis of this post is correct. It is also irrelevant – very few people would confuse an extension of a past trend into the future for an infallible prediction.

    Consider for a moment how the intelligence of animals and, finally, humans evolved. Did it start with sophisticated statistical methods and philosophical rigour? Of course not. We have two cats at home – one smart, the other not so much. The smart one is extremely good at picking up cues. For example, at the breakfast table, I first have some toast and jam, which she doesn’t seem to pay any attention to. However, afterwards, I make some salami or chicken sandwiches for my brown bag – and she is immediately on it and reminds me of her presence. Her cue? A little break between toast and sandwich. If I happen to pause a bit longer between two slices of toast, she will show up prematurely. (This is a simplified version – she follows a whole set of cues, she really is a smart beast, and as a consequence, too fat.)

    What happens if she is wrong? She walks off to return again later, and the next day she will still try the same – because this strategy soundly beats a random guess. That’s what our brains give us – educated guesses. Of course, it’s nice when we can go beyond that, but much of the time, this is what we do, and as the course of evolution shows, there is nothing wrong with it.

  76. “…fanatical button collector. He collects buttons at every chance, stores them away, thinks about them every day, reads about buttons and button collecting, spends hours every day sorting his buttons into different little boxes and bins and worries about safeguarding his buttons…”

    On reading this, did anyone else think of Stanley Howler, Moist von Lipvig, Terry Pratchett and pins? :-) Now I have to go read “Going Postal” again.

    As to the article, trends in the absence of any other data simply provide us with the initial assumption what its doing now is what it will keep on doing, for now. As the trend is studied and its actual (and possibly-multiple) causes are sorted out, the importance of the trend as a predictor diminishes, and the factors that caused the trend become more important.

    w/regard to global warming, it appears that its proponents have spent 20 years denying any and all factors responsible for the trend except for their initial assumption that it was all CO2.

    They will be the laughingstock of future history books, taking their rightful place alongside the Millerite movement’s “Great Disappointment” and of course Piltdown Man.

  77. Nassim Nicholas Taleb, The Black Swan: The Impact of the Highly Improbable

    E. T.Jaynes, Probability Theory: The Logic of Science.

    Connecting the dots on an epistemological map ignores the complexity between the dots, however closely they are placed. Reality is fractally complex.

  78. Michael Palmer says: October 18, 2013 at 6:28 am “The thesis of this post is correct. It is also irrelevant – very few people would confuse an extension of a past trend into the future for an infallible prediction. ” Still we indulge in progressive historicism. Santayana’s dictum was a caution and not a prescription for action. Karl Popper, The Poverty of Historicism, and The Open Society and Its Enemies.

  79. The academics will tell you that you are describing a purely empirical model and therefore extrapolation is not accurate. Now the learned academics build “first principles” models and therefore extrapolation is possible. For example, one can theoretically calculate the trajectory of a projectile based only on velocity and angle of departure from the horizon (assuming no air resistance, etc.)

    But that’s where the learned academics fool themselves: they don’t really ever know the complete first principles model. Friction, air resistance, etc. combine to make the full calculation impossible for all but the most idealized cases. Even artillery calculations utilize “fudge factors” that are required to dial in the targeting calculations for the given situation. Accounting for powder charge, mass, velocity, wind speed and direction, temperature, etc. can get you close but not guaranteed “bullseye”.

    The climate is no less complex and no one has a crystal ball.

  80. The use of statistical tools by “scientist” who do not understand the mathematics is a big problem. I agree that linear trends on small samples from a large complex non-linear population have little predictive quality other than finding out that the system is non-linear. Weather forecasters generally hedge their bets and limit their “predictions” to a few days.

  81. Fred the Statistician says:
    October 17, 2013 at 10:38 pm
    Will the sun rise in the East tomorrow?
    ========
    The sun will rise in a direction that is mostly eastward, but this will change day to day and if you based a prediction of where it would rise in the future months based where it was last month, you would be right or wrong largely as a matter of chance.

    Cyclical data does provide predictability, based on the observation that nature tends to be cyclical, otherwise extinction events at the extremes would have long ago made us the observer extinct. Trying to fit linear approximations to cyclical data is a statistical nonsense.

  82. David L. says:
    October 18, 2013 at 6:58 am
    But that’s where the learned academics fool themselves: they don’t really ever know the complete first principles model.
    ========
    The classic example is tidal prediction. Prediction from first principles is a hopeless way to try and predict the ocean tides. Instead we predict the tides with great accuracy using a method that is for all intents and purposes astrology.

    We observe the tides and the position of the sun, moon and planets in the heavens, and predict that the same alignment in the future will result in the same tides. We don’t need to know what causes the tides, there is no need for a mechanism, only the observation that nature moves in repetitive cycles.

    Early humans learned to predict the seasons the same way, long before they could predict them from first principles. We could predict summer and winter long before we understood the tilt of the earth on its axis around the sun.

  83. fhhaynie says:
    October 18, 2013 at 6:59 am
    Weather forecasters generally hedge their bets and limit their “predictions” to a few days.
    =====
    Climate forecasters make their “predictions” so far in the future that no one can check their accuracy.

  84. @ Doug Huffman

    “Connecting the dots on an epistemological map ignores the complexity between the dots, however closely they are placed. Reality is fractally complex.”

    Though I certainly agree with you on this, a great deal depends on your interpretive framework. I think our problem is often that we look at the map through the wrong end of the telescope (quantitatively) and everything seems so distant and out of reach, not to mention rather one-dimensional. A bit like a soporific general surveying a battlefield with his field glasses back-to-front and remarking at how far away the enemy seems and how flat the terrain. This framework just doesn’t reflect reality satisfactorily. We intuitively sense that ‘there is much more to this than meets the eye (than we can currently measure)’.

    Yet if we can retrain ourselves to view the map more qualitatively (fractally, quantum-ly), perhaps –just perhaps – we might find ourselves looking through the other end of the telescope and seeing the lay of the land with greater clarity. We would then see a more intricate and layered topography of interconnecting factors (with both quantitative and qualitative properties) that better reflects reality. And better predicts probable outcomes while accommodating those “most tenebrous of cygneous waterfowl”.

    So while I agree with you, I think we need to take a more positive approach, embedding the best of what we currently have in a wider and more qualitative framework, rather than just pointing out the obvious failure of the current paradigm.

  85. This entire article is categorically untrue. Correlation is not causality, agreed, but it is all we ever have to make sense of the Universe. Science is nothing but systemetized observations of correlation that are indeed interpreted as probably being causality. The author should really a) learn some statistics; and b) read Jaynes’ Probability Theory, the Logic of Science.

    The point isn’t that one cannot be mistaken in e.g. the extrapolation of a linear trend. The point is that one can often make some fairly powerful statements about how probable it is that one will be mistaken. If the author’s primary point above were true, then just because we’ve observed that objects released from rest close to the Earth’s surface consistently have fallen down according to what appear to be simple, predictable rules that fit consistently into a framework of similar rules for our entire life, we would still have no good reason for believing that the next time we drop a penny or throw a baseball, it will follow a trajectory consistent with those past observations. I will cheerfully bet the author $1 a trial for as many trials as he likes that if either of us drop a penny, it will fall down. I’ll even give him odds. Hell, I’ll just plain give him a dollar the first time it falls up.

    There is so much more that I can say that is mistaken about this analysis. It ignores Bayes’ theorem entirely and the value of priors and their effect on estimates of future marginal or conditional probabilities. It ignores all of the mathematics associated with functional analysis (e.g. the Taylor series) which basically asserts that if there is an underlying non-stationary distribution from which any set of statistical samples is drawn that meets some very, very broad requirements — continuity, differentiability — that in fact one can almost invariably extrapolate a linear trend for at least some time, simply because the linear term often dominates a Taylor series expansion of the underlying distribution. That doesn’t mean that it always will, or that there don’t exist distributions and processes where it never will, but that there is a very, very broad class of processes for which it will, in fact, work. Broad enough that I dare say it will probably work nearly all of the time.

    Even in climate science, this trick works remarkably well. What’s one of the best predictors of tomorrow’s weather, one that doesn’t rely on satellites or all of the tricks given in Anthony’s lovely book on predicting the weather (which, by the way, are also a direct contradiction of the top article, as the top article is basically saying that Anthony’s book is nonsense because just because certain cloud patterns were observed to have a linear correlation with future weather in the past doesn’t mean that those correlations will persist into the future)? “The weather tomorrow will be pretty much like the weather today”. Why? Because if one computes the autocorrelation of a variety of aspects of the weather, the autocorrelation time is longer than a day for many of them. Large temperature shifts don’t occur daily, they occur every few days (and even then, usually occur within fairly narrow ranges). Sunny fine days are often clustered (because high pressure systems are usually large enough and move slowly enough that they take days to pass over any given point). Ditto rainy/cloudy weather. Note that we cannot be certain of any of this, and that at some times of the year and on some parts of the globe the autocorrelation is smaller (in the middle of the Sahara or Antarctica I imagine it is positively huge, but in the temperate zone springtime it is comparatively short) but even this is known, approximately, on the basis of observed, extrapolated linear trends buffed up only in the modern era by additional Bayesian prior knowledge such as an understanding of the physics underlying moisture and the movement of air masses and cloud formation and so on.

    Red sky in the morning, sailor take warning dates back 2000 plus years and is nothing but a linear extrapolation of observational data in weather systems. Now we actually understand things like Rayleigh scattering and why the rule — more often than not, but not at all certainly — works.

    So sorry, if you want to actually learn about the statistical science of the improbable, you are far better advised to rely on Taleb’s book The Black Swan, who makes the same point the author is attempting to make, only far, far better, and in a context that fully appreciates that even black swan events don’t invalidate the assertion that autocorrelation in systems with an internal (hidden) dynamics is usually long enough that linear extrapolation of their behavior is valid for some time (times less than the empirically observed autocorrelation time, in fact), they simply mean that one should hedge one’s bets taking into account the possibility of comparatively rare but highly costly exceptions.

    rgb

    [Should "because just because" be "just because" . ? . Mod]

  86. But that’s where the learned academics fool themselves: they don’t really ever know the complete first principles model. Friction, air resistance, etc. combine to make the full calculation impossible for all but the most idealized cases. Even artillery calculations utilize “fudge factors” that are required to dial in the targeting calculations for the given situation. Accounting for powder charge, mass, velocity, wind speed and direction, temperature, etc. can get you close but not guaranteed “bullseye”.

    Excuse me, but this too is bullshit. Learned academics (such as myself) who teach this stuff every day do not, in fact fool themselves. Often we teach our students “this is idealized bullshit, but it is still the first step towards understanding what goes on and is at least approximately correct”. And if one works hard enough, and wisely enough, within the known limits of our knowledge and descriptions, one can do things like build the laptop you are typing this on, or naval fire control computers that work well enough to sink ships remarkably well (compared to the old days of pirates firing cannons at point blank range).

    Don’t generalize. I know you’re trying to say “Climate models suck” but why not just say it instead of accusing “academics” of not knowing the limitations of their own knowledge in general?

    rgb

  87. Replies to All:

    To those who have been supportive, Thank You.

    To those who have been generally supportive but have some concern or question: Thank You, I’ll try to cover your concerns as I answer comments in buckets by issue raised.

    To Statisticians Everywhere: Fred, Ted and a couple of others. Luckily, I am not a statistician. I write as a professional “practitioner of practicality” — as a “practician”. Statisticians have their own definitions of words that are different than the rest of the world and the things discussed in my essay mean different things to them. They are sure their definitions are the correct ones, even though the rest of us don’t use the words that way. All fair enough. If some statistician would like to translate my essay into Statistician-ese, I would be glad to read it. The practical principles presented in my essay — however simplistic and lacking in nuance — are nonetheless correct in what even lawyers are now required to call “plain English”.

    and

    An Observation: I am pleased to observe that here at WUWT, as opposed to the Dot Earth blog, in the 104 comments so far, there has not been a single instance of rank name calling or bullyism — not one. Marvelous.

  88. 2. Models, not trends, can predict, project, or inform about possible futures, to some sort of accuracy. Models must include all of the causative agents involved which must be modeled correctly for relative effects. It takes a complete, correct and accurate model of a process to reliably predict real world outcomes of that process.

    The rigor specified here is overstated and in the end entirely false.
    Ancient astronomers were able to predict lunar and solar eclipses without knowing all the causative agents. They might have even been wrong is important aspects of the causative model. The Farmer’s Almanac makes useful predictions about where and when to plant, not from accurate causative non-linear models, but from historical records and employing trends.

    An accurate prediction based upon causative agent models, of Breckenridge snow base on Dec. 26, 2013 would be most difficult. An accurate prediction for Dec. 26, 2053 would be even harder. Nevertheless, using only trends based on historical data, you can have extremely high confidence in a prediction that snow base for Breckenridge on July 4, 2054 will be much less than it will be Dec. 26, 2053.

    [PREDICTIVE] Models can and should be tested by their abilities to correctly predict already known values within a data set of the process and then tested again against a real world future.
    The value of any model must be measured by the difference it can make to you with the prediction in hand[1] compared to without the prediction. No matter how mysterious, if a model had a 60% chance of correctly predicting BLACK on a roulette wheel the model would have great value indeed.

    Note 1: Sometimes your value in a model lies solely in fact that you found some sucker to pay you for a prediction.

  89. Statistics and probability are tools like hammers and saws. Experience, understanding and intelligence have to be applied to use an appropriate tool for a specific job. Hammers and saws are fairly easy to understand but still need skil to apply well, whereas statistics and probability require a deep understanding together with a deep understanding of the process/system under consideration before the appropriate tool can be chosen and skillfully applied. Just applying formulae to collections of numbers is not the way to use statistics intelligently in the same way it is unreasonable to pick a specific process (button collecting) and suggest it invalidates the intelligent use of statistics.

  90. Reply to those insisting that trends can and/or do, or can be used to, predict the future:

    It is important not to confuse apparent trend with model output or results of an underlying process. In the BC example, the first ‘trend’ presented is three button counts, on three successive days. There are simple measurements (counts) and it is really an error to call them a trend, as a trend implies or assumes that they are the result of a [modeled] process. Drawing a line through the data points does not change anything. Think about this difference. Don’t make the assumption for evidence not presented. A simple count or measurement is just that and nothing else. One mustn’t assume an underlying process (which would or could be modeled to produce data). Try another example — the value of the coins found in your pocket at the end of each day, which you dump into a jar on the dresser. Count this every day for a week. Graph them and see some trend. Can you scientifically use this trend to predict the next day’s value? next Wednesday’s value? No, of course not. It is not, as some commenters have pointed out, because it is random — it is because you have no reliable information about the process that produces “coins in my pocket daily”. You can not even formulate a mental model that allows estimation of the future. If you make any prediction, whether your prediction is right or wrong — or right 75% of the time — you are fooling yourself with numbers.

    Trends formed by model output–when the model has been formulated with knowledge about the process it models–do have predictive ability. It is the model that has the ability to predict, not the visualization of it on a piece of paper or a computer screen. For us to make a prediction using a model, we look for things like trends. Many models are far more complicated and the predictive power is NOT made evident by straight-line linear trends, but by other indicators. The predictive ability of models always depends on the correctness of the model and the accuracy (and appropriateness) of the input.

  91. Now that here has been an opportunity to digest Jaynes’ ‘Converging and diverging views'; this is why narrators of controversial stories must use considered and measured tones, for the least hyperbole wedges the audience at a greater rate. Lessons the AGW-ists and media must take to heart to regain any measure of credibility.

    If RGB at Duke has an academician’s grasp of Jaynes’ maths then this retiree would appreciate an on-line course so narrowly focussed. I will read ALL of Probability Theory, however slowly, however interrupted by Popper, Taleb, Kahneman, Tversky …

  92. Yes I think we can agree on the usefulness of simple observations like pencil’s dropping and the sun rising but when things get a lot more complex perhaps the Climatology Club might need to think about parallels in science.

    From The Australian (18th Oct) comes a report of this piece of settled science from an impeccable source like the IPCC presumably-

    THE World Health Organization yesterday classified outdoor air pollution as a leading cause of cancer in humans.
    “The air we breathe has become polluted with a mixture of cancer-causing substances,” said Kurt Straif of the WHO’s International Agency for Research on Cancer.
    “We now know that outdoor air pollution is not only a major risk to health in general, but also a leading environmental cause of cancer deaths.”
    The IARC said a panel of top experts had found “sufficient evidence” that exposure to outdoor air pollution caused lung cancer and raised the risk of bladder cancer.
    Although the composition of air pollution and levels of exposure can vary dramatically between locations, the agency said its conclusions applied to all regions of the globe.
    Air pollution was already known to increase the risk of respiratory and heart diseases.
    The IARC said pollution exposure levels increased significantly in some parts of the world in recent years, notably in rapidly industrialising nations with large populations.
    The most recent data, from 2010, showed that 223,000 lung cancer deaths worldwide were the result of air pollution, the agency said.
    The data did not enable experts to establish whether particular groups of people were more or less vulnerable to cancer from pollution, but Dr Straif said it was clear that risk rose in line with exposure.
    In the past, the IARC had measured the presence of individual chemicals and mixtures of chemicals in the air — including diesel engine exhaust, solvents, metals, and dust.
    Diesel exhaust and what is known as “particulate matter” — which includes soot — have been classified as carcinogenic by the IARC.
    The latest findings were based on overall air quality, and based on an in-depth study of thousands of medical research projects conducted around the world over decades.
    “Our task was to evaluate the air everyone breathes rather than focus on specific air pollutants,” said the IARC’s Dana Loomis.
    “The results from the reviewed studies point in the same direction: the risk of developing lung cancer is significantly increased in people exposed to air pollution,” he added.
    The predominant sources of outdoor air pollution were transport, power generation, emissions from factories and farms, and residential heating and cooking, the agency said.
    “Classifying outdoor air pollution as carcinogenic to humans is an important step,” said the IARC’s director Christopher Wild.
    “There are effective ways to reduce air pollution and, given the scale of the exposure affecting people worldwide, this report should send a strong signal to the international community to take action without further delay.”
    The IARC said that was set to publish its in-depth conclusions on October 24 on the specialised website The Lancet Oncology.

    Now from those lung cancer statistics it may be Big Tobacco rightly want an international apology and their money back. What say you Big Climate? Is the science settled here or are Big Tobacco being a little too simplistic perhaps?

  93. Kip Hansen said:
    October 18, 2013 at 8:09 am
    An Observation: I am pleased to observe that here at WUWT, as opposed to the Dot Earth blog, in the 104 comments so far, there has not been a single instance of rank name calling or bullyism — not one. Marvelous.

    ———————————————–
    Idiot.

    Now gimme your lunch money.

    ;)

  94. Reply to those who feel that the conclusions of my essay invalidate or threaten science or statistics or use of their favorite cookie recipe:

    Many commenters object that “we can and do predict things all the time” and give examples such as flying bullets, cannon balls in flight, rolling cars, the sun rising and setting, tides, historical snow falls and a long list of other things. And that we use observed “trends” to do so. Of course we do.

    But why can we do so, since trends cannot and do not predict the future? It is because of what we are substituting for “trends” in these examples – in most cases we are substituting mental models based on physical processes. We see a car rolling straight across the parking lot, sans driver, towards a playing child. We automatically formulate the model for free rolling cars, use the model to make a prediction, rush over and pick up and remove the threatened child from danger. There is no trend involved in this example. There is the [mentally] modeled output of the physical process of the rolling car (based on well-understood and time-tested Newtonian laws of motion).

    Another factor in this general concern bucket involves the subject of forecasting. [ http://www.forecastingprinciples.com/ ] Forecasting is a vitally important subject. See the link provided for scientifically formulated forecasting principles. Many mention directly, or intuitively, that a first principle of forecasting is that your best bet, when faced with a complex system about which little is understood, is to forecast “more of the same” and then use this to mean that one’s best bet is to always predict that a trend will continue. I am not a forecasting expert any more than I am a statistician. But before one can apply this true principle of forecasting, one has to apply a higher and overriding principle of forecasting, which is to first determine is any meaningful forecast can be made at all, given the problem, the data available, one’s understanding of the processes involved and the purpose of the forecast. I consider it most likely that simply predicting linear projection of a trend is not a valid forecasting method – unless one steps way way back and looks at the long long term behavior — such as “Investing in Blue Chip Stocks is a good long term investment approach”.

  95. rgbatduke says:
    October 18, 2013 at 7:46 am

    Agreed, more or less. The author has shown a particular type of behavior is not predictable using trends. However, a linear regression is actually the optimal mean-square estimator for a particular type of random sequence: that of a deterministic affine variable with random slope and intercept, with measurements polluted by uniform independent noise. That is, in fact, the model for which a standard linear regression is derived.

    He would be on much more solid ground if he said that climate variables, particularly mean surface temperature anomaly, do not behave like such a sequence and, as a result, are not predictable to the desired level of accuracy using such a model. To the degree that your model fails to capture the dynamics of the actual process, statistics derived based upon that model are dubious, to say the least.

    A better model for mean surface temperature anomaly could be constructed. It would include a process model for the 65 year dynamics observable here, and possibly the 21 year process as well. The 65 year process is key – it was the upswing of that dynamic which was mistaken for an anthropogenic effect in the latter decades of the 20th century, and it is that process which is currently driving temperatures back down again. All independently of egocentric humans who, like a flea on the elephant’s back, think they control the beast because he happened to turn when the flea thought “turn!”.

  96. mhx says:
    October 18, 2013 at 2:06 am
    Suppose that a car at a distance of 500 meters starts driving in your direction and for 490 meters it drives in a straight line at constant velocity. The car has no driver. It has been programmed. You don’t know the program. You know nothing about the causes that make the car drive in a straight line for about 490 meters. What would you do?

    ———————————————————–

    Stay on the sidewalk. It is the driver using a cellphone that worries me.

  97. Kip, as you and others acquainted with this subject no doubt know: Statistics is the language of science, but Probability is the logic of science. Most people have never come into contact with either the language or the logic, and therefore fill in the gaps in their education with arm waving and emotion.

    How many people do you know who are familiar with Hume, Jaynes, or others of that caliber?

  98. People make critical decisions based upon statistical analysis and trend presentation all the time, included educated and well-reasoned people who make it their living, including actuaries, financial analysts, marketing analysts, etc. Complex sets of data won’t always yield rational explanation, and the fast-paced world of real business can’t afford the luxury of the time it takes to completely isolate all the variables to come to root cause understanding. There is a cost to not making a decision, and opportunities often have a shelf life, and therefore regression trend analysis may be the only predictive tool which has both the timeliness and utility to give answers.

    While it is easy (and perhaps a bit enjoyable) to poke fun at “scientists” who hang their hats on statistical trending and modeling, it would be disingenuous to say there is zero value in using modeling as a predictive tool. Indeed business and significant portions of science would be severely handicapped without it. While I agree with the premise of the article that people who use statistical analysis and models need to understand the limits and risks of the forecasting it can produce, I would avoid making such a sweeping indictment.

  99. I’m not sure what the frequent reference to being able to predict a rising sun has to do with a description of the nature of what a trend is? Instead of using a phenomenon that is modelled VERY well and then claiming the hypothesis that trend has predictive value is validated (c’mon man!) why not pick an actual test of your hypothesis?

    Take the last 100 months of temperature data from any station you want, fit a trend to that data and bet me 5 quatloos that you know what the tempature will be tomorrow. Afterwards, I’ll give you the money back if you promise to read this essay again.

    see this arcticle by statistician William Briggs for more on what trend means and what it doesn’t mean.

    http://wmbriggs.com/blog/?p=6854

  100. Just an engineer;
    Stay on the sidewalk. It is the driver using a cellphone that worries me.
    >>>>>>>>>>>>>>>>>

    No no no! You use YOUR cell phone to hack into the car’s computer and slam on the brakes. They do it on TV all the time, everyone knows that. ;-)

  101. Opinions vary on the causes and meanings of these wanderings and on what the future may bring.

    But of one thing you can be certain – and you may verify this with any Wall Street day trader or professional gambler — Recent trend – up, down, or dead flat – does not determine the future. If it did, they’d all be rich.

    You can hardly talk about predictability of a trend until you have verified its accuracy and reliability. There are parallels between investors and those with a vested interested in climate, and it’s worth noting that both groups rely heavily on the conventional databases that archive “past performances”. Of those two data sets, which is more reliable? Personally, I trust Wall Street market data more than I trust the archives about the climate at, for example, UEA HadCRU. Perhaps too many eyes are on the DOW minute by minute for investors to suffer the kind of blatant “adjustments” of their database that has affected the government-corrupted climate industry.

    In his graph here a few days ago, Roy Spencer documented fully 90 climate model projections from the 1980’s which incorrectly mimicked each other (see http://wattsupwiththat.com/2013/10/14/90-climate-model-projectons-versus-reality/ ). Only a few of these came close to the observed temperature records (HadCRUT4 Surface, and UAH Troposphere) strongly suggesting that they were biased by the same incentives to achieve the same alternate reality. Programmed to go upwards, they went about their predetermined task with alacrity. Do such diversions from “reality” happen with stocks? Every day, it appears, at the whims of lying CEOs and intrusive government agents attempting to pull the strings of the economy. Sooner or later, however, sometimes with brutal determinism, the markets correct themselves. By instantly clarifying its own record, such “efficiency” earns investment indexes a grudging respect not earned by climate records, and arguably makes predictions of the movements of the DOW more reliable.

  102. Kip Hansen:

    Respectfully, I write to disagree with your assertion that “trends cannot and do not predict the future”.

    In your explanatory post at October 18, 2013 at 9:48 am you say

    But why can we do so, since trends cannot and do not predict the future? It is because of what we are substituting for “trends” in these examples – in most cases we are substituting mental models based on physical processes. We see a car rolling straight across the parking lot, sans driver, towards a playing child. We automatically formulate the model for free rolling cars, use the model to make a prediction, rush over and pick up and remove the threatened child from danger. There is no trend involved in this example. There is the [mentally] modeled output of the physical process of the rolling car (based on well-understood and time-tested Newtonian laws of motion).

    Sorry, but the human brain is very good at assessing trajectories (i.e. trends in the spatial changes of objects). Ball games would not be possible if this were not so.

    I assure you that a person dodges a falling object on the basis of a prediction of where the trend of the objects movement will lead to it hitting the ground. The only model is the trend.

    Similarly, an athlete does not have a “[mentally] modeled output of the physical process” of a ball’s flight “(based on well-understood and time-tested Newtonian laws of motion)”. She assesses the ball’s trajectory and predicts future position on the basis of the non-linear trend of its movement. And as e.g. wind alters the trajectory she constantly adjusts her prediction as she runs to where she hopes to catch it. Indeed, a bowler in cricket uses several methods (e.g. swing, spin, bounce, etc.) to disguise the eventual trajectory of the ball from the batsman.

    People use trends as predictors every day. Evolution has honed ability to do that because it works more often than not. Indeed, this evolutionary result is why people are good at observing patters even where no patterns exist.

    A trend can and does predict the future so long as the trend continues into the future. But trends change with time and, therefore, trends are imperfect predictors of the future.

    Richard

  103. Nothing predicts the future exactly. Statistical trends, with or without adequate knowledge of the underlying process, predict the near future better than anything else, where “better” is measured by “mean squared prediction error”. In central Missouri, next December will be cooler than next July, even though the process of weather is incompletely known; and in central Missouri the weather tomorrow will be more like the weather yesterday than like the weather 3 months ago, almost all the time. An investment company that has above average returns 10 years in a row will have above average returns next year, but will display regression to the mean; that is true for almost all investment companies that have above average returns 10 years in a row.

    Even if you know the mechanism you need measurements on its outcomes and estimates of its parameters, and a statistical analysis of how well the computed model has fit the data in the past. The prediction of the future based on knowing the mechanism will be a calculation based on the computed estimate, along with a probability distribution over the range of possible outcomes.

    To repeat: nothing predicts exactly; trends do better than anything else.

    I think people forget to make comparisons among alternatives, and neglect to specify what they mean. When you specify a measure of successful prediction, like mean square error, mean absolute error, etc., and then look at all available alternatives, then trends are the best of a pretty dismal lot.

  104. Richard S Courtney: Sorry, but the human brain is very good at assessing trajectories (i.e. trends in the spatial changes of objects). Ball games would not be possible if this were not so.

    Today we are in agreement. That’s an excellent example.

  105. Some random replies:

    “How many people do you know who are familiar with Hume, Jaynes, or others of that caliber?” None, including myself :-) I am a lowly “practician”.

    Howard Booth (representing a substantial number of similar comments) says: “While it is easy (and perhaps a bit enjoyable) to poke fun at “scientists” who hang their hats on statistical trending and modeling, it would be disingenuous to say there is zero value in using modeling as a predictive tool.”

    My essay pokes no fun at anyone (well, possibly a bit at my eccentric acquaintance, The Money Collector, who actually exists, btw). My essay is about common misunderstandings regarding the naïve idea that trend lines on graphs of things can predict the future. There are important scientific fields called statistical modeling and forecasting. Both are perfectly valid in their own realms and both have very specific rules and procedures and principles that must be followed to ensure that their results are useful and valid. Mr. Booth, and those with this concern, need only read the comments to my post at Dot Earth—[ http://dotearth.blogs.nytimes.com/2013/10/09/on-walking-dogs-and-global-warming-trends/ ] —to understand why this simplistic essay has been written. While I am sure that the truth is perfectly clear to you (and others with a similar concern), there are persons who profess to believe that trends determine future values, in and of themselves.

    In regards to Non-Linearity: To almost whatever you have to say about it, I say “I agree”. I have followed Lorenz and non-linearity since my youth and so, YES, to whatever you want to say about it and its ramifications on the subject of trends, models, and processes. Most of the comments revolve around this: the modeling of a non-linear system, even if it is composed of a single simple wholly deterministic formula used re-iteratively, produces results that can be said to be chaotic and “unpredictable”. Most here at WUWT agree with the IPCC that the Earth’s climate system is a “bounded non-linear chaotic system”.

  106. Kip Hansen says: October 18, 2013 at 9:48 am “But why can we do so, since trends cannot and do not predict the future?”

    E. T. Jaynes, Probability Theory, 3.2 Logic vs. propensity (p 62) “In all the sciences, logical inference is more generally applicable. We agree that physical influences can propagate only forward in time; but logical inferences propagate equally well in either direction.”

    I’ve highlighted this paragraph, while most of my notes are in pencil. I remembered it as; cause and effect are constrained to the arrow of time, logic not so. This paragraph follows, three paragraphs after, a criticism of Popper’s ‘propensity’.

  107. meemoe_uk says:
    October 17, 2013 at 7:38 pm
    author is getting on a high horse over words…

    I couldn’t agree more! The essay you posted on this topic was much more informative and riveting. Thank you for taking the time to educate the audience and to put your thoughts out for public criticism.

    Eric

  108. Reply to richardscourtney (October 18, 2013 at 11:13 am) :

    I have no wish to embarrass Richard but there are quite a few comments that use this or a similar logical fallacy to confuse themselves about trends:

    He says: “Sorry, but the human brain is very good at assessing trajectories (i.e. trends in the spatial changes of objects). Ball games would not be possible if this were not so.”
    As I have pointed out previously, confusing “trajectories” with “trends” will lead you wrong every time. A trajectory is not a trend – they only look the same when graphed on a piece of paper. A trajectory is the path of a moving object based on the laws of Newtonian physics—it is part of an ongoing physical process. It is not a numerical construct or the output of a numerical model (though one could easily model the process and add the details of a trajectory that one could use to place the Rover gently on the surface of Mars). Conflating trajectory—a physical process—and trend—a numerical report about the past of a process—is a grievous logical error.

    And “A trend can and does predict the future so long as the trend continues into the future. But trends change with time and, therefore, trends are imperfect predictors of the future.” It is the physical process that is likely to continue into the future—the trend is simply a report and has no existence until new data are added moment by moment. The model will tell us if the physical process is likely to produce a new datum in line with the past trend. To say that “all of my correct predictions will be correct if they are correct and my incorrect predictions incorrect” says nothing more than “sometimes I guess right, sometimes I guess wrong.”—there is nothing there about prediction, it is just guessing—reference Feynman regarding scientific guessing.

  109. “He will have between 0 and a googleplex of buttons. I’m 95% certain of it.

    I’m 100% sure he’ll have between 0 and infinity (inclusive).”

    Arts graduate’s view of the above: Why limit oneself to just that range? Those pesky scientific types are SO unimaginative, so black-and-white, so obsessed with right and wrong.

  110. @ Doug Huffman says:
    October 18, 2013 at 11:40 am

    Re: Jaynes

    Good to know someone else who has studied him. :) I’m sure, then, that you’ve also heard the following:

    “Probabilities do not exist.” And: “In the absence of Reality, Probability rules.” Both of which require considerable explanation which I won’t go into here, and the concepts can be difficult for many to wrap their heads around.

  111. I am one of the one’s you told not to read it, because I already understood it, but I read it anyway, apparently there are still a lot people around here who still don’t understand it.

  112. @ Kip Hansen says:
    October 18, 2013 at 11:28 am

    Some random replies:

    “How many people do you know who are familiar with Hume, Jaynes, or others of that caliber?” None, including myself :-) I am a lowly “practician”.
    ****************************************************************
    As was I for many years. But, I’d recommend Jaynes to you anyway. Here’s a link you can pursue at your leisure: http://omega.albany.edu:8008/JaynesBook.html

  113. …Current performance may be lower or higher than the quoted past performance, which cannot guarantee future results…

    Investors get to read these informative words or phrases that are very similar on virtually investment returns page.
    Why?
    Well, these people are held accountable otherwise.

    “Steve Obeda says: October 17, 2013 at 7:06 pm

    “The only correct answer is: We have no idea how many buttons he will have Friday night because we cannot see into the future.”

    — I would say that states things a little more harshly than is justified and that a more correct answer is “We cannot be sure, but the trend indicates a best guess of…” I forecast using trends all the time. A trend is useful even if it is only partially informative. No, we don’t know the underlying equation and we might be wrong but in many cases it doesn’t matter.

    What’s missing here is the intended use of the forecast. If your boss is asking you to forecast sales, you’d better not tell him that there’s no way to know for sure. Before firing you he’ll say he knows that, but the business needs to make decisions on inventory and capacity. It doesn’t matter if you’re a bit wrong because you’ll simply adjust the forecast. But if your boss is deciding whether to decarbonize the global economy at a cost of several trillion dollars, then the strength of your model had darn well better justify it. Otherwise, the answer is going to be to keep studying and keep him posted on your progress, but hold off on the big spending.”

    That never makes the trend accurate or even mostly correct. It is a deliverable, period. Try using the ‘official cya’ chit that financial institutions use to protect their butts. Your boss will get just as mad and it won’t make your trend any more accurate. So the trend providers who don’t like suffering often provide updated trends as frequently as their data sources update. That’s the only way to account for changing variables; like the economy tanking overnight and customers stop buying immediately and distributors start bellowing about unsold product while your suppliers are demanding payment for inventory just delivered… Oh yeah! Trends told your boss everything.

    A trend is old the day it is made and it’s age escalates as time trickles by,

    “Fred the Statistician says: October 17, 2013 at 10:38 pm

    “The trend is utterly useless as a predictor — always.”
    This is absolutely incorrect. In the absence of any other data, past trends are the BEST predictor of future values.

    This entire article shows a complete failure to understand statistics. Statistics can be used to make predictions, based on certain assumptions. If those assumptions are correct, the prediction is MORE LIKELY to be correct. If the assumptions are wrong, then the prediction is LESS LIKELY to be correct. No where are guarantees made…
    …”

    “Fred the Statistician says: October 17, 2013 at 10:38 pm

    The correct prediction in the article would have been “Given the assumptions we are making (iid, normal, etc), we predict that it is most likely that there will be 110 buttons, give or take (a very large number).” No statistician would EVER say “There WILL BE 110 buttons.” Because statisticians actually understand statistics.
    … “

    Assumptions! Which preferably are accurate observations masquerading as assumptions.

    Unfortunately today’s observations are minimal and everything later is unknown, totally unknown. While the computation gives a ‘confidence’ interval it is rarely very comforting when one hands trends off to the boss or public.
    It may be an educated guess, but it is still a guess.

    “Fred the Statistician says: October 17, 2013 at 10:38 pm

    “We can’t know the future” is an utterly silly argument. Will the sun rise in the East tomorrow? I hope you didn’t say “yes”, because you can’t know the future! If I let go of this pencil I’m holding in the air, will it fall to the ground? I hope you didn’t say “yes”, because you can’t know the future!
    … “

    Ever do a trend on the sun? What did you use for assumptions?

    If you drop your pencil, it will fall to the ground because gravity or the ‘earth’s bent space’ condition is active. Your dropping or implying you are going to drop a pencil is ‘not’ a trend! It is a single action. Now if you always drop your pencil three minutes prior to leaving work, that we can trend. But only the act of you dropping the pencil, gravity is still a relative constant (i.e. any changes are undetectable to casual or even intensive observation).

    If all assumptions are not only correct, but stay reasonably consistent for the time duration covered by the trend; then we can make an accurate guess.

    Yeah, I did trends for a large organization; some daily, some weekly, everything on the accounting period, fiscal year and for 5, 10, 20 year plans. Five years plans are a joke as they rarely were worth much beyond “What in the world did we think we were going to do?” historical amusement, mostly because some executive belief’s were masquerading as assumptions and as soon as executive’s change or their minds change, that major assumption is toast.

    I also learned to check daily journal (accounting) entries watching for surprise hits.

    When someone would demand to know when I would deliver an accurate plan (trend); I would usually respond with “As soon as you can tell me exactly how many workhours will be used, product delivered, items purchased, capital equipment purchased and facilities built and maintained.”

    Repeat! If all assumptions are not only correct, but stay reasonably consistent for the time duration covered by the trend; then we can make an accurate guess.

    Guess what major blind faith movement is betting everything that has consistently proved their assumptions are wrong?

    Perhaps we should be doing a performance trend on how models, modelers, and modeler PR people?

  114. Kip Hansen,

    Your ‘Buttons’ has attracted an excellent statistical audience with lively metaphysics / epistemological discourse. Thanks. It doesn’t get more enjoyable than this.

    Hey, just yesterday I started on Jaynes’ book and rgbatduke’s partial draft of his book project ‘Axioms’. So, my statistical paradigm is being tested right now. I’ll need to get back to you later on your ‘Buttons’.

    Personal Note=> I was quite critical of Popper’s science theory related thesis starting in the 1980s after reading Brand Blanshard’s ‘Reason and Anaylisis’ but now with Jaynes and Brown input I anticipate becoming more critical of Popper.

    John

  115. Kip Hansen:

    I am replying to your post at October 18, 2013 at 11:55 am in response to my post at October 18, 2013 at 11:13 am).

    You say

    I have no wish to embarrass Richard but there are quite a few comments that use this or a similar logical fallacy to confuse themselves about trends:

    I made no “logical fallacy” and you do not cite one. The only person whom you “embarrass” is yourself by your answer to my point.

    As always when involved in a semantic disagreement, a definition of terms is needed. In this case, there is a dictionary definition and a mathematical description of “trend”. This is the definition according to the Online Dictionary.

    trend (trnd)
    n.
    1. The general direction in which something tends to move.
    2. A general tendency or inclination. See Synonyms at tendency.
    3. Current style; vogue: the latest trend in fashion.

    Clearly, we are discussing “1. The general direction in which something tends to move.”

    And in statistics, a trend is detected and observed in a time series is modelled. This is what the ‘Statistics Glossary’ says

    http://www.stats.gla.ac.uk/steps/glossary/time_series.html

    Trend is a long term movement in a time series. It is the underlying direction (an upward or downward tendency) and rate of change in a time series, when allowance has been made for the other components.

    A simple way of detecting trend in seasonal data is to take averages over a certain period. If these averages change with time we can say that there is evidence of a trend in the series. There are also more formal tests to enable detection of trend in time series.

    It can be helpful to model trend using straight lines, polynomials etc.

    Clearly, you are plain wrong when you say of me

    He says: “Sorry, but the human brain is very good at assessing trajectories (i.e. trends in the spatial changes of objects). Ball games would not be possible if this were not so.”
    As I have pointed out previously, confusing “trajectories” with “trends” will lead you wrong every time. A trajectory is not a trend – they only look the same when graphed on a piece of paper. A trajectory is the path of a moving object based on the laws of Newtonian physics—it is part of an ongoing physical process.

    A trajectory is
    “The general direction in which something tends to move”
    and it can be recorded as an incremental time series
    which can then be analysed to determine the form of the trend
    that can be plotted on graph paper.

    You have confused the statistical record of the trend as being the trend itself. And the actual trajectory IS the trend itself.

    You are also wrong when you quote me and dispute my quote saying

    And “A trend can and does predict the future so long as the trend continues into the future. But trends change with time and, therefore, trends are imperfect predictors of the future.” It is the physical process that is likely to continue into the future—the trend is simply a report and has no existence until new data are added moment by moment.

    No, the trend is ““The general direction in which something tends to move” and the “report” is of that “general direction”. The trend will end probably because the physical process which causes the trend will amend or ended, but the trend is NOT the physical process; the trend is a result of that physical process(es) which could be random chance.

    And I am fully conversant with Feynman.

    Richard

  116. @ John Whitman says:
    October 18, 2013 at 1:09 pm

    Kip Hansen,

    Your ‘Buttons’ has attracted an excellent statistical audience with lively metaphysics / epistemological discourse. Thanks. It doesn’t get more enjoyable than this.
    ****************************************************************************
    Indeed. :) Of course, all of this is our feeble human attempt to discern future Reality. So from a probabilistic pov, we must first define what reality is. The prevailing definition is when P=1, which is commonly termed an “Event” (hopefully a desired outcome). So the decision is one of choosing the set of probabilities that will result in the desired outcome, based on our level of knowledge and our influence over the process that (we hope) will lead to that outcome. Since we don’t have perfect knowledge we cannot have perfect confidence in our actions to attain that desired outcome. The Black Swan is always hovering. You pays your money, and takes your chances. :)

  117. We do trend analysis in education all the time. Rate of improvement is one way to determine whether or not learning pace is sufficient to catch up to grade level. Unfortunately, many educators do not know what they are looking at, don’t use a defensible form of calculating a linear trend, and do not use this analysis appropriately even when calculated correctly.

    For example, when measuring reading speed improvement over time many focus on the linear trend and ignore the data spread. Not a good thing to do. I rarely see a tight time-dependent correlation, instead seeing data spread above and below the linear trend in odd herky jerky fashion. Yet educators use the trend line to adjust instruction, assuming that instruction is the driver of the trend line and individual data points can be safely ignored.

    Nothing could be further from the truth. Data spread is where the focus should be, not on the trend line itself. Sometimes the reason for far flung performance above and below the trend line has nothing to do with instruction. So changing instruction in hopes of a better trend, or worse, measuring teacher performance on such a metric, is patently wrong. And for the exact reasons outlined in the post above.

    4 marks.

  118. Reply to Richard:

    We’ll just have to see what others think about your view.

    We can’t discuss it if you insist that trajectory of a cannonball is really a statistical trend and I maintain that the trajectory (the physical path that the baseball has followed so far and its future projected path based on Newtonian physics) of the baseball is an actual physcial world process. I maintain that even though the words used describing the two are very similar, they are not the same nature of thing.

    My view includes the point that I could model the path of the baseball, and my model, because it is based on Newtonian physics, would produce a fairly precise projected tragectory for the ball. The projected path would look like a “curving trend” on a graph, but it is not a statistical trend.

    Thus, we’ll have to leave it for another time. Thank you for participating.

  119. Hi, Kip. You said, “My essay is about common misunderstandings regarding the naïve idea that trend lines on graphs of things can predict the future. [ ] While I am sure that the truth is perfectly clear to you (and others with a similar concern), there are persons who profess to believe that trends determine future values, in and of themselves.”

    I’m having a hard time understanding why you are persisting in these arguments. You now seem to be saying that there are people out that who believe that dots on a page with a trend line drawn through them is actually the causal agent that determines the future. And (or so it seems) you are arguing rigorously against this proposition.

    It is my feeling that, in your zeal to dissuade these phantom prognosticators, you have gone too far in the other direction, and made some statements that are also wrong.

    No one believes that a trend on a graph is a causal agent. It is completely and utterly understood, as Richard Courtney stated above, that the data + analysis is merely the statistical record of what has occurred in the real world. As such, that analysis – using time-tested methods – can give a solid prediction of the future. How solid that prediction is, and at what level of precision, depends upon the quality and characteristics of the data, and the skill of the statistician performing the analysis.

    I gave you the best version of an empirical model that I could think of: gravity. We do not know anything about the causal agents of gravity (other than, as I said, a few recent hypotheses that have not yet been proven). Yet we know very precisely how gravity behaves. We know this (and I am trying to stick to ‘common sense’ language, as you stated is your preference) only because of a vast number of measurements that fit very well into fixed and consistent formulas. It is wholly an empirical theory. Newton came up with the main formula based (mainly) on observations of planetary orbits; Einstein refined Newton’s findings based (mainly) on measurements of the speed of light. The theory of gravity is thus consistent, but not with any need or reliance on causal agents – it is consistent with our observations. It is empirical.

    When someone says “The trend predicts X,” what they mean is that the discovered pattern is robust enough to make a solid prediction about the future. You can argue that the level of certainty is too high, or that the level of precision is overstated. (Depending on the particulars, of course.) But you cannot say that an identified trend has no information to offer about the future.

    In your BC example, you gave a very short and simple trend: 102, 104, and 106, on three successive days. And then you said – and here is where you went too far – that this trend tells us “absolutely nothing” about the number of buttons on day five. In other words, you state that day five’s value can be anything; that 110 is just as likely as 3; or 57; or 72,639; or ten billion. I would say, instead, that 110 is the best guess, and is more likely to be right than any other value (even though, with only three data points, it is not a very certain guess).

    Statistics can quantify this guess and tell us the probability that it is correct. In fact, that is all that statistics does. I don’t think I really understand what part of this you don’t agree with.

    Cheers.

  120. John Whitman: Do you have a link to “rgbatduke’s partial draft of his book project ‘Axioms’.”?

  121. Doug Huffman: Re Jaynes’ “We agree that physical influences can propagate only forward in time; but logical inferences propagate equally well in either direction.” IMHO, this is what allows us to use both inductive and deductive reasoning to work out models from known facts and forward to facts from known models. Not my area of specialty.

  122. Kip Hansen:

    re your post at October 18, 2013 at 2:25 pm.

    I am not impressed with your sign off of, “Thankyou for participating”.

    I have pointed out and explained that you are confusing the result of a set of physical processes (i.e. a trend) as being the physical processes.

    The trend will change when the processes alter or end. And that is why a trend is a future predictor although an imperfect future predictor.

    You say the trend is not a predictor but the set of physical processes is.

    If you were right then farmers would not have planted their crops at the right time of year for millenia. Fortunately, farmers understood what the trend of past seasons predicted although they had no knowledge of orbital mechanics.

    As you said, thankyou for participating.

    Richard

  123. Yes, cited above! Particularly Section 5.3 ‘Converging and diverging views’ (pp 126 – 132) excerpted here

    http://www.variousconsequences.com/2009/11/converging-and-diverging-views.html

    I have a different but related example that addresses the same sort of thing. In The Tenth Kingdom movie/series, close to the end, Tony and his friends encounter an enchanted frog in front of two doors. The frog croaks out “One door leads to freedom; the other door leads to certain death. You may ask me one question, but I always lie!”

    Tony proceeds to rant about how silly it is (even in fairy stories) to have a door that leads to certain death guarded by a lying enchanted frog, picks up the frog and throws him through one of the two doors. Explosion. Wolf says “I guess it is the other one”.

    This is a marvelous example in logic as well as being funny as all hell. What does Tony know? Well, the frog says that it always lies. This is a statement that literally cannot be true — “I always lie” is necessarily a lie because it cannot be true. However, nothing stops one from having a frog that sometimes lies.

    Confronted with a frog that cannot be counted on not to deceive you, what’s the best course? Empiricism! Test the doors to find out! And what better to use to test it with than lying frog!

    That still doesn’t prove that the OTHER door doesn’t lead to certain death, and of course it does. All doors lead to certain death. Some perhaps sooner than others…;-)

    rgb

  124. Ted Carmichael: Have you been trained in statistics? My guess is yes, you have. That’s the problem here. I am not trained in statistics, I am a “practician” (practicing the art of the practical).
    You want to talk statistics and probability.

    As a practician, I insist : The numbers 102, 104, 106, given in order can inform us of nothing whatever except the counts of buttons those particular three days (and the offered fact that they were counted, not weighed or averaged or something). That’s it. Nothing else — nothing possible, nothing probable, nothing about tomorrow’s value at all. I can say this with 100% certainty because we, as yet, know absolutely nothing whatever about the process represented by these numbers, except our expectation they will be counted again tomorrow.

    Please don’t go on if you haven’t at least read all my replies so far on this post, you are already forcing me to repeat stuff I have already repeated several times.

    Now, quite honestly, if you hope to make your point, you must have a logical, scientific, or even theological reason for believing that you can make any kind of forecast or prediction at all in the utter absence of any understanding of what is being predicted.

    I will be glad to listen directly to whatever Statistical Expert you may wish to supply in this regard — but I don’t think that anyone will say you can make a prediction from a position of total ignorance.

  125. “How many people do you know who are familiar with Hume, Jaynes, or others of that caliber?” None, including myself :-) I am a lowly “practician”.

    (Raises hand…)

    I have no idea what a “lowly practicianor” is in the context of statistics. Either you understand the concepts and have worked through the math (in which case you understand precisely how, and in what sense, and under what conditions statistical trends are predictive) or you are just saying lots of things that are not, as I pointed out, true. In which case you would be well advised not to write entire untrue and misleading articles.

    Myself, I’m on my second company founded on predictive modelling. We sell a service that is worth money — quite a lot of money — because you are incorrect. If you were, in fact, correct, we’d instantly go out of business. Not only do we build predictive models, we do so without any strong underlying assumptions concerning the nonlinear function that describes the joint probability distribution we are modelling. They are effectively almost purely empirical models, in other words, with only a few Bayesian assumptions that go into the structures. I understand this stuff very well indeed.

    Hence my suggestion that you START by learning some actual statistics before you make egregiously incorrect claims about statistics (which not only I, but pretty much every person knowledgeable about the subject have tried to correct you on, in many cases with nearly identical comments and yet independently — in fact, there is a trend here that I expect to continue as we continue to sample the knowledgeable fraction of WUWT readers…hmmm:-).

    Again, if your goal is to justify a statement such as “GCMs suck!” feel free to make the statement, and there are many ways to justify such a claim. But don’t try to justify it by stating “predictive models, statistical or otherwise, never work”. That’s nonsense. As a previous poster noted, if one builds a model correctly, the “trends” it extracts from the data are indeed in a mathematically rigorous sense the best prediction of the future, almost always able to an assumption of complete ignorance of the future. There is literally a century of statistical theory, maximum likelihood, maximum entropy, Bayesian reasoning, and more to justify this. Exceptions to this principle are empirically rare — indeed, truly “unpredictable” means “maximum entropy” in a sense that is rarely realized in nature, in the sense that outcomes are completely, truly, random. If you read David MacKay’s online book on Artificial Intelligence and Information Theory (which is hard going, be warned) you will learn about information redundancy and compressibility and perhaps get some insight into why your assertions are not only wrong, they are deeply wrong.

    rgb

  126. Kip Hansen on October 18, 2013 at 2:34 pm said,

    @John Whitman: Do you have a link to “rgbatduke’s partial draft of his book project ‘Axioms’.”?

    – – – – – – –

    Kip Hansen,

    I see rgbatduke has already responded to you with a link.

    John

  127. A better model for mean surface temperature anomaly could be constructed. It would include a process model for the 65 year dynamics observable here, and possibly the 21 year process as well. The 65 year process is key – it was the upswing of that dynamic which was mistaken for an anthropogenic effect in the latter decades of the 20th century, and it is that process which is currently driving temperatures back down again. All independently of egocentric humans who, like a flea on the elephant’s back, think they control the beast because he happened to turn when the flea thought “turn!”.

    Dearest Bart,

    We don’t always agree, but Damn! We agree on that. Indeed, as I’ve been posting elsewhere, HADCRUT4 can be modelled almost perfectly (well within the visible noise in the underlying process with a four parameter fit — a straight line with a roughly 0.2C sinusoid with period somewhere in the 55 to 70 year range (the data is too imprecise to do much better) Since HADCRUT4 is indeed an anomaly, the base of the linear part is irrelevant, so this is really a three parameter fit as one degree of freedom doesn’t count.

    If one counts the degrees of freedom that are CLAIMED to be important in GCMs, there are at least ten — one can list things like TOA insolation, albedo, shortwave and longwave absorptivity, heat capacity of Earth and atmosphere, and that’s six parameters in a single slab model that averages over all rapid processes, in the simplest POSSIBLE model that actually illustrates a GHE. It is claimed that CO_2, that did not change in any relevant way until roughly 1950, is responsible for the sudden temperature upturn in the 1980-2000 era, when it could not have possibly been responsible for the almost identical upturn in the 1920-1940 era, when a single THREE parameter fit works across the entire interval with no change in functional form precisely where we have to expect one if CO_2 is indeed a proximate cause of global warming.

    This requires a stunning coincidence between multiple nonlinear parameters in order to work numerically, and that coincidence is, in fact, rigorously statistically unlikely.

    rgb

  128. rgbatduke: I suspect that you too are a Statistician or something close to it. You Statisticians do not own the world — despite your claim to God-like certainty or at least your probability of having God-like certainty of the probabilities. egads…you’d think I had been burning your sacred texts in public or wearing your Holy Statisticians Robes to a frat party.

    Jesting aside, the problem here is the child-like simplicity of what I have to say. It is nothing more or less than actually stated. We do not speak the same language — you have your ingrained classical statistical definitions and seem to fail to grant anyone else a right to have a parallel correct understanding. You want to make this some great thesis on statistics and probability.
    Have you done your homework, your part in trying to understand what I am saying? Did you read the Dot Earth essay, as suggested, and the comments there? Do you even realize that we are talking about different things? I simply state that trends are not themselves causative agents, they are effects.

    You know perfectly well that drawing a line through three dots (numbers) on a piece of graph paper is a meaningless activity. If we know nothing but those three numbers thus plotted, they tell us nothing. Forget all the statiscianishness. Three points on a piece of graph paper, with or without a line, tell us nothing. The same three points, identified as button counts on three successive days, still tell us nothing more than that. No amount of statistics will make this as yet unknown system reveal any more secrets — certainly not the future.

    Maybe you and Ted and Fred could get together and defend the sacred halls of StatsWorld by explaining the question I posed for Ted above http://wattsupwiththat.com/2013/10/17/the-button-collector-or-when-does-trend-predict-future-values/#comment-1452514. How can you use statistics to predict under conditions of total ignorance of system? I give you three numbers….make a scientifically defensible prediction based on them–you may make no assumptions.

  129. Hi, Kip. Okay, I understand now. You think a three-day trend has absolutely no information. As you say, “Nothing else — nothing possible, nothing probable, nothing about tomorrow’s value at all.” Extrapolating from that, if – as you say – there is zero information in a three-day trend then, similarly, there must be zero information in a 30-day trend, or a 300-day trend.

    Don’t you see that if your friend adds two buttons a day for, say, 342 days, then it’s a pretty good bet that he will add two more buttons on the 343rd day? It would have to be something quite exceptional, by the common-sense definition of the word, for this trend to end at exactly the point when you make a one-day prediction.

    My brother has tried to argue something similar. He believes that all polls are useless; that it is impossible to survey 500 or 1000 people, and assume the results of that survey can give us any information about what the rest of the country thinks.

    My brother is very smart, but he doesn’t understand statistics at all. Cheers.

  130. One more quick note: you said, “I simply state that trends are not themselves causative agents.” Kip, thank you for clarifying this. I would like to assure you that no one believes a trend is a causal agent. Trends are correlated with causal agents, and therefore – even if we don’t know what the causal agents are – we can gain some information from a trend.

    But I’m afraid “trends are causal agents” is the strawest of straw-man arguments. No one makes that mistake, even if (as with the dog-on-the-leash post) their wording is somewhat sloppy.

  131. @rgbatduke at 2:54 pm
    I don’t know about the frog….but your story was enchanting.

    The layers of logic were as rich as a pastry.
    Especially that last: “All doors lead to certain death. Some perhaps sooner than others…”

  132. Ted — You must be a statistician. If you can still say this ” I would like to assure you that no one believes a trend is a causal agent. ” you have not done your homework. Read to comments at Dot Earth. And some here….You may not believe it….I certainly don’t. Since that is not an issue for you, and you agree with me 100% on that, why are you going on and on? My essay clearly states that the model is predictive. You somehow can’t get past the fact that the model VIEW you prefer is represented by a graph that may show some trend therefore the trend is …what? You are arguing about what I, as an internet programmer, would call a “view” — the database is back there, and I present various views of it with my web pages. My web pages are not the database, they are simply a single out of many views of it. It is not that there is zero data in a three day trend–there is zero data (above the three numbers) because none has been supplied.

    (Remind me not to hire you as a fortune teller…..or to tell me whether to buy this gold mine stock based on three supplied ore sample numbers. )

    Let me know when you are prepared to supply a prediction from three numbers alone — no system – no assumptions ………..and please, be prepared to defend it scientifically.

  133. Ted — Don’t worry, Briggs supplied me with a demonstration of the mean and probabilities of just a single number! You guys must have some wild parties!

  134. The rules that the button collector uses could change over time given future changes to his collection then even if we asked what the rules were going to be tomorrow he would not be able to tell us. In my opinion this is true of the model of how the Earths temperature changes over time and why we can’t know what the global temperature is going to be a hundred years in the future .

  135. Statisticians, including the usually amazing rgbatduke:
    Not that you are wrong, but you are substituting belief for knowledge. I’m glad you believe in your statistician skills.

    Yes! If you’ve analyzed every assumption and condition one can build a reasonably accurate trend.
    Two buttons a day for an extended period does allow one to ‘reasonably confidently’ state one more day means two more buttons.
    Only, reality is lurking with it’s own set of conditions. One possible condition is that two buttons has exhausted all button sources and instead one gets zero buttons added per day till new sources are located.

    Yes, one can build companies that specialize in providing modeling services and you reap a financial benefit for being good at it. “…Not only do we build predictive models, we do so without any strong underlying assumptions concerning the nonlinear function that describes the joint probability distribution we are modelling. They are effectively almost purely empirical models, in other words, with only a few Bayesian assumptions that go into the structures…”
    And every time the models are not up to snuff or even if something bothers you, they’re torn apart till you identify exactly what degraded the estimate.
    The basic truth is; your confidence drives a ‘belief’ in your models. You’re d__n good, so your confidence is high. Does this mean you always hand your model report over to a customer without explanations, caveats, concerns? Of course not! Your team(s) analyze every run until you’re sure the bases are covered. No customer should ever get a model’s report until they understand what’s involved.

    You are working out probabilities not absolutes. I’m glad you’re good and running a series of successful model runs; that doesn’t bring you close to omnipotent. Instead when you nail a forecast I’ll bet you’re jubilant, and you have every right to be. I’ll also bet you have a good idea when a model run needs work before handing off.

    Sure, it’s a sucker bet to bet something will always fall when dropped, unless somehow something goes wrong with physics. That is not a trend, nor is it a forecast. The suckers who take the bet are deluded.

    But, what if you’re dropping a down feather? A fluffy feather has enough surface area as compared to mass to make it vulnerable to other forces that are locally stronger, like wind or even one’s breath.
    I’ll consider that bet now. Still willing to make it?
    You don’t ‘win’ the bet by stating any model based on dense object trends; the bet is only won when the feather is ‘observed’ to fall down.
    Sucker bets always pay off, reality bets are a risk. A risk that is mediated by high confidence model trends, but risks are never converted into confidence absolutes.

    Gentlemen; you’re d__n good; both at your work and your comments here.

    Kip:
    I like your article. I do think you have a perspective that is different than some of the statisticians. I don’t think this makes you wrong, but be aware how perspectives affect our thoughts. The statisticians writing here are quibbling about some definitions. What’s missing is a lack of realization that when a statistician insists models can be accurate, they’re making that statement with full awareness of exactly what they consider accurate, within bounds unaffected by anomalous factors. They’re not magicians nor are they omnipotent; they’re well educated skilled humans.

  136. @ Ted

    You said:

    “Extrapolating from that, if – as you say – there is zero information in a three-day trend then, similarly, there must be zero information in a 30-day trend, or a 300-day trend.”

    And you are absolutely correct. A 30-day trend has absolutely zero predictive value for day 31!!!

    Do we have to go back to flipping a (close to truly random) coin heads or tails to get the point that a trend is NOT predictive at all?

    10, 100, 1000 heads in a row, does not change the fact that it is just as likely to be a tails as a heads, on the next flip, even though the trend screams heads.

    I certainly hope that no one on this board would posit that the trend of 10000 heads in a row has even 1 infinitesimal bit of predictive information regarding the outcome the next flip.

    If one argues that many heads in a row may indicate a trick coin, it may — but the law of large numbers supports 10,000 heads in a roll even for a random coin.

    Either way — we would need knowledge of the fairness of the coin (underlying causal agent !!) to make the trend a useful predictor — it must be a trick coin for the trend to be predictive.

  137. “John Andrews says: October 18, 2013 at 10:05 pm

    Tomorrow is going to be just like today, only a little different.”

    Making fun of us huh? Good twist of humor of a forecast using a trend. :)

  138. “… a pretty good bet… ” may turn out to be a winner, but the bottom line is

    it is merely an educated

    guess.

    A sudden gust of wind can alter a trajectory.
    An unexpected twist of the jet stream can alter local climate.

    Trends are useful tools; they make poor masters.

    We will be RULED by the junk trends of those worse-then-useless IPCC computer simulations if we lose the fight for truth about what drives climate (and what clearly does not).

    Kip Hansen’s pointing out that we do not “know what a day may bring forth” is a POWERFUL argument against CAGW (regardless of the statistical nuances that he chose to overlook).

    Let us not devour one another — we’re in this fight for truth to WIN.

    At WUWT, we’re on the front lines of the battle.

    “In essentials, unity.
    In non-essentials, liberty.
    In all things, charity.”
    This motto will keep us strong.

    While the other side shrieks at and backstabs each other — go for it Fantasy Science Guys! — we maintain a united front for the essentials and, thus,

    TRUTH WILL WIN.

  139. Janice Moore:

    Thankyou for the rallying cry to unity in your post at October 18, 2013 at 11:50 pm.

    As you say, eventually,

    TRUTH WILL WIN

    and we seek ‘truth’ as a method to refute the false claims of alarmists.

    It follows from this that we need to proclaim truth and to refute falsehood.

    It is a falsehood that “trends cannot and do not predict the future”.

    The truth is that
    A trend can and does predict the future so long as the trend continues into the future. But trends change with time and, therefore, trends are imperfect predictors of the future.

    And that truth is everybody’s common experience. Before people knew why day followed night they assumed it would because it always had: and that trend was a reasonable predictor that dawn would break tomorrow.

    Proclaiming falsehoods which are not in agreement with everybody’s experience does not provide credibility when we proclaim truth.

    Richard

  140. Unfortunately, the alarmists do not see ‘truth’ in quite the way ‘realists’ do. Many ‘Realists’ believe, and some even claim to know, that ‘out there’ there exists an objective world – the ‘true’ world that is available for observation by seekers after truth (normal scientists). The contrary view is that our view of the truth is socially constructed and that science is just another vehicle for imposing one world view on others – this is rather quaintly called ‘post-normal science’. Heavily disguised as truth seeking, this is no more or less than a fraud contrived to achieve some political aim – such as the realisation of Agenda 21 – if that is strange to you, just Google it up for a big surprise. That is only one example of the host of ’causes’ riding the post-normal bandwagon. Indeed anyone who thinks that the Hand of Destiny is on his/her shoulder is a welcomed passenger. Even Paul Nurse, President of the Royal Society, contributes to this simple pragmatic view in the name of drumming up cash for research such as his microbiological research centre being established in the heart of London and incalculable cost to taxpayers (i.e. everyone).

    I have suggested elsehwere that it is not reason that drives the emergence of ‘institutions’ from conversations but the comforts of togetherness that arise from participating on language games (yes old LW again), that these institutions colonise and consume human lifetime is the cause of their own survival and this goes for all belief systems however zany.

    All this is quite ‘natural’ and simply a feature of humanity – but it really is perverse and if we think that ‘institutions’ serve human interest, we probably have fairies at the bottom of the garden too.

  141. Before the moderator suggests it, I would like to withdraw the reference to Paul Nurse and offer the following amended version…

    Unfortunately, the alarmists do not see ‘truth’ in quite the way ‘realists’ do. Many ‘Realists’ believe, and some even claim to know, that ‘out there’ there exists an objective world – the ‘true’ world that is available for observation by seekers after truth (normal scientists). The contrary view is that our view of the truth is socially constructed and that science is just another vehicle for imposing one world view on others – this is rather quaintly called ‘post-normal science’. Heavily disguised as truth seeking, this is no more or less than a fraud contrived to achieve some political aim – such as the realisation of Agenda 21 – if that is strange to you, just Google it up for a big surprise. That is only one example of the host of ’causes’ riding the post-normal bandwagon. Indeed anyone who thinks that the Hand of Destiny is on his/her shoulder is a welcomed passenger.
    I have suggested elsehwere that it is not reason that drives the emergence of ‘institutions’ from conversations but the comforts of togetherness that arise from participating on language games (yes old LW again), that these institutions colonise and consume human lifetime is the cause of their own survival and this goes for all belief systems however zany.

    All this is quite ‘natural’ and simply a feature of humanity – but it really is perverse and if we think that ‘institutions’ serve human interest, we probably have fairies at the bottom of the garden too.

  142. Jan Smit says: October 18, 2013 at 4:21 am

    @acementhead

    Point taken, sorry for being so sloppy! However, it was only meant as joke about how statistics can be used to assert any nonsense (hence the winking smiley). But I consider myself suitably chastised…

    Oops, sorry I guess I overdid it. Must confess that I failed to see the smiley. I claim, in mitigation, very late hour and refusal to wear glasses even though eyes almost time expired.

    Here’s a good link to support acementhead’s correction of my spurious nonsense:

    http://www.prb.org/Publications/Articles/2002/HowManyPeopleHaveEverLivedonEarth.aspx

    Do you agree though, acementhead, that statistics can and are used to support all manner of claptrap?

    Yes I most certainly do agree with you on that.

    Cheers

  143. Quite some time ago here on WUWT, regular commenter Anna V pointed out that “You can never cross the same river twice.” The earth, the solar system, the galaxy, the universe are on unique paths that started somewhere and will end somewhere else. So I really appreciated this comment;

    John Andrews says:
    October 18, 2013 at 10:05 pm

    Tomorrow is going to be just like today, only a little different.

    I would add that every once in a while, tomorrow turns out to be very different as, for example, that day where the earth was struck hard enough to blast the material for our moon out into earth orbit. Though I probably won’t be around to see it, one day the sun is not going to rise. That’s a good long term prediction. Short term I’m betting on old sol popping up tomorrow.

    As Richard points out just above (richardscourtney says: October 19, 2013 at 3:04 am), the predictive value of a trend is only good for as long as the trend lasts.

  144. A comment aimed at acementhead and Jan SmitThank you for being an example of how it is possible to work out misunderstandings and smooth over little offenses cause by “the heat of the moment” in a public forum. If only all of us would work so hard to work through and resolve these types of differences. Well done.

  145. Reply to ATheoK (October 18, 2013 at 10:58 pm):

    Thank you for your well-nuanced, well-mannered and thoughtful contribution. Of course, what you say is right. Of course, what our resident Statisticians say is right within their own frame of understanding and given their own very well-trained adherence to their specialized definitions. Unfortunately (or fortunately–opinions vary) we do not all speak that language nor do we all have the training to think in those specialized ways—we need to understand things in the context of our lives and experience.

    It is seems obvious to me that when our Statisticians say “a trend” they actually refer to (what I call) the underlying model that produced the trend and conflate the two into a single entity – thus speak of the trend having predictive capabilities.

    While, as a budding blog author (for years I have simply commented on topics that interest me) who has just recently stepped out onto the public stage, I have gained some experience with the comments section in which abject name-calling and intellectual bullying are part and parcel of the course. Plenty of that at Revkin’s Dot Earth. I was caught by surprise though being mobbed by a tag-team of (mostly) polite professional academic statisticians, including, arguably, one of the finest minds in the world. It has been a learning experience.

    Thank you again.

  146. Reply to karl (October 18, 2013 at 11:03 pm) :

    Thank you for your supportive comment.

    I think the points that I would take away from your discussion of truly random systems are these:

    1. In order to deal with any trend on a time series, one absolutely must (with apologies to rgbatduke) know something of the system being represented—the system that produced—that is ‘caused’—the metric considered in the time series. Doesn’t matter if it is buttons-in-jar, GMST, blood serum levels of some biological indicator, or sunspots. Before using the apparent trend to make a prediction, once must know at least something about the system—the physical process itself—what we are talking about here!
    If, as in your coin toss, the process is entirely perfectly random, then we must use a model of a random process to make our prediction. With a model for a two-sided fair coin, the prediction must be (real world here please!) 50% probability of heads and 50% probability of tails—which in a practical world sense, is no useful prediction at all.
    2. Most of the physical processes that we encounter in our day-to-day and scientific lives are not perfectly random. Thus, the things we wish to be able to predict need a different model. Even processes that seem simple can require quite complex models to produce practically useful results. We must not incorrectly assume randomity. Examining past data (trends, spreads, anomalies, etc.) helps us to determine the rules (causative agents) in the process under consideration and enables us to produce a functional model that is adequately predictive.
    3. In almost all of the examples offered of the use of trends successfully predicting, the “trend” offered is what I have tried to clearly delineate as the model—even if just an assumed, automatic mental model—of the underlying process.

    Thanks for your contribution here.

    • For those who do not trust the use of statistics in data analysis, I suggest you read http://books.google.com/books?id=8C7pXhnqje4C&printsec=frontcover&source=gbs_ViewAPI#v=onepage&q&f=false. This is based on a paper I presented at an international corrosion symposium way back in 1971. It was published as a chapter in a handbook and has been revised and published twice since then. I think it is still valid. If you don’t understand how to use statistical techniques, you should talk to a statistician and at least learn from them. The probability that you will make mistakes in your research will be greater when you don’t use statistical techniques correctly. Much of the the IPPC research is a good example of missuse. Ask most any statistician.

  147. Kip Hansen wrote:

    “Reply to Richard:
    We’ll just have to see what others think about your view.”

    Yeesh, this has to one of the most depressing and pointless threads on WUWT in a long time. Thanks (again) to Richard Courtney and Dr. Brown for injecting some sense. Hansen’s bilious argument can be demolished with the simplest of concepts: if you are attempting to predict the future, it is wise to do the best you can with what you have. If you have empirical information, that is a good place to start!

  148. Steve Obeda says:
    October 17, 2013 at 7:06 pm

    You overlook your own tacit knowledge. If you rigorously developed and maintained metadata on your “forecasts” then that metadata would emerge. It is still not prediction in the scientific sense. But there is information in your metadata that serves as practical reasons for your forecast.

  149. Jquip says:
    October 17, 2013 at 8:38 pm

    “Nor is there any necessity, or even sanity, in attempting at self-fulfilling logical positivist approaches to causality. Newton went commando with Hypothesis non Fingo. Kepler was a pure data fiddler. So was Galileo. Point of fact, any postulated cause has less expectation for correctness, in general, than a long chain of validated predictions arising from a non-causal model. And that’s putting aside any expected issues that arise from the field of physics and it’s distinct reliance on impossible objects taken to every infinite asymptote they can find.”

    I would be happy to debate you on this matter if you would make substantial claims. Your claims here amount to nothing more than trolling. Are you really willing to claim that Kepler’s work and Newton’s work cannot serve as exemplars of scientific method?

    Also, on the matter of prediction, you speak with the vulgar. You write:

    “Point of fact, any postulated cause has less expectation for correctness, in general, than a long chain of validated predictions arising from a non-causal model.”

    One cannot assume, as you do, that the term ‘prediction’ has a clear and defensible meaning in your sentence. Your job is to explicate the meaning of ‘prediction’. In other words, whether “predictions arising from a non-causal model” actually qualify as scientific predictions is the issue being debated. The loose way that you state matters requires that you explicate “non-causal model.”

  150. davidmhoffer says:
    October 17, 2013 at 9:28 pm
    I liken it to a person walking up a hill and down the other side, and measuring their altitude at every step. Even if we know exactly what the length of their stride is, we cannot predict from the past data what their altitude will be on the next stride unless we know exactly what the shape of the hill is. In fact, if all we have is the data from the first 25% of the journey, the data would provide a trend suggesting the person is headed for outer space.

    Interestingly, if we stopped our data collection when the person was 5 steps past the crest of the hill and on their way back down, we could say, and correctly so, that the person’s last 10 steps were the highest in the entire record of the journey. It would not change the fact that the person is going down hill.
    +++++++++++++++
    I was thinking this concept of an explanation to this fine Post! The alarmists are focused on the past 10 years being some of the “warmest in recorded history” because we are near the top of the recent short term relatively natural warm period.

    Janice: You have a clear way of looking at information!

  151. Chapter 5. in the 1995 second edition. I am not a statistician. My expertise was in atmospheric degradation of materials (especially the corrosion of metals).

  152. Oh, Mario, you don’t know how much I needed to hear that. Thank you. And, thank you for taking the time to tell me. I am so grateful that you are a regular on WUWT. And, you do, too — with the added benefit of your scientific education and expertise. I hope that, like A-th-y, you are enjoying a happy weekend with your family. Take care. J.

    GO, NUKES! #(:))

    (uh oh……. I think I hear him (won’t even mention his name — whince)…… stomping over this way right now…… time to go, uh, to go…… get my driver’s license renewed … or something… ANYTHING. Heh.)

    **************
    Thank you, too, Richard, for your 3:04am today affirmation of the spirit of (though, not the content of) my post. How kind of you to lift me out of the Slough of Despond which is the only downside to the GREAT FUN posting on WUWT is: being ignored (or the illusion of it which, like optical illusions, tricks the mind for a bit into thinking things are what they are not).

    **************
    @F. H. Haynie (you will never “retire,” you know — and so glad you are here, still teaching) — thanks for the helpful cite. So…. your “… expertise was is in atmospheric degradation of materials (especially the corrosion of metals),” hm? They could use you over at Save the Planet Jewelery, Inc.. Can you turn lead into gold? That’s what they are working on right now! Oh, maaaan, those Fantasy Club guys are sooooo funny (and easy to mock).

  153. Kip Hansen says:
    October 18, 2013 at 11:55 am

    “Reply to Richard:

    We’ll just have to see what others think about your view. “
    ——————————

    Kip H, this poster pretty much agrees with all of your commentary. It was/is refreshing to read said commentary that was/is not “laced” with the author’s personal biases of the subject matter being discussed, ….. and/or commentary of a PJE nature. Statisticians will claim “importance” of their statistics because it would be silly of them not to.

    Statistics based on past observations of cultural activities are useful “tools” for projecting the potential continuation of and/or a future trend associated with cultural activities ….. simply because humans are “creatures of habit” whose habits are highly influenced by the actions and deeds of other humans and which are subject to change depending on the likes and dislikes of said humans.

    Statistics based on past observations of the earth’s physical activities are also useful “tools” but only for the research and discovery of the causes and/or effects of said physical activities ….. simply because Mother Nature is not a “creature of habit” and therefore has no habits nor any likes or dislikes of any physical event. And without any habits, likes or dislikes there can be no trend or prediction of any trends. And the aforesaid is confirmed by the stated definition of the word “trend”, to wit:

    trend 1. The general direction in which something tends to move.

    Everything in the universe “tends to move” ….. depending on one’s “point-of-reference”. Even the general direction of the observed retrograde motion that a few of the planets tend to move, ….. but in actuality they don’t.
    Ref: http://www.lasalle.edu/~smithsc/Astronomy/retrograd.html

    One can “squeeze” a general direction “trend” out of most any data set if the data set is large enough. Trends are like beauty, ….. they are in the eyes of the beholder.

    Trends can not predict the future. Only an observer of a trend is capable of making a prediction.

    And trends do not change, they just cease to be applicable in some environments and/or are replaced by a new(er) trend.

  154. Hi Janice:

    You wrote “And, you do, too — with the added benefit of your scientific education and expertise. ”
    ++++++++
    That’s kind of you Janice. Compared to some people, I am pretty up on the climate sciences. But compared to many here, I am a neophyte. I enjoy being allowed to play with people who are above my level of knowledge! I want to make sure that the folks who comment a lot at WUWT don’t think I am full of myself. My quest is to find what is true and try to wrap my head around it.

    I’ve found that most people have their minds made up about certain subjects because they affiliate with a group and consider themselves like-minded. Most of these people do not understand what research is. They watch a documentary or read something in a newspaper and consider that doing research. They are now tainted like a red blood cell with a carbon monoxide molecule bonded to it!

    I remember when I first started seeking the truth about climate (after seeing Gore’s movie) I was researching NOAA’s website to find out how they adjusted the data to filter out the urban heat island effect. I can no longer find what I read there. But to paraphrase from memory, they used 5 algorithms (I think) based on demographic information to account for growing population etc – and then apply factors to remove the additional warming of urban temperature readings. The person who wrote that section of the site wrote something like “…and we know that these algorithms work because the adjusted temperatures increased in line with our expectations.”

    I was shocked and could not understand how people could not be skeptical of NOAA?

  155. Thank to you all for reading and commenting. I will not be answering individual comments from this time forward — I have another couple of projects that are reaching the time-intensive stages.

    If any of you have questions or comments you feel I need to answer, please feel free to contact me directly at kip at my domain i4.net.

    Blue Skies and Following seas,

    Kip Hansen

  156. “Mother Nature is not a “creature of habit” and therefore has no habits”

    So the Earth invert its rotation or even rotates along longitude?

  157. I think the problem of this text is the over desire to explain things with binary concepts and simple words, the seeking of elegance of simplicity even if it is not possible in several cases. This reminds me of Climate Science today and CO2 which is worse.

  158. “… algorithms work because the adjusted temperatures increased in line with our expectations… .” (NASA website quoted by Mario Lento)

    Well, you gotta admire their candor!

    LOL, CO2 in the blood and ON THE BRAIN. Good one.

    ***************
    Hi, Alex S (at 7:11pm) — The intrinsic merit of Kip Hansen’s post aside, your equating his approach to the Fantasy Science Club’s CO2 conjecture is inaccurate.

    The climate models of the IPCC suffer far more from OVER-elaboration and needless complexity which, as some famous scientist I can’t remember (he’s quoted in Bob Tisdale’s book Climate Models Fail) said is: the sign of mediocrity.

    Oh, bother — …. just went to look it up. Here it is from page 36 of Climate Models Fail — Box! That’s the guy’s name…:

    The IPCC in Chapter 8 of their 4th Assessment Report provides a brief explanation of climate models. They write near the beginning of their Frequently Asked Question 8.1:
    Climate models are mathematical representations of the climate system,
    expressed as computer codes and run on powerful computers.

    In other words, climate models are an attempt to simulate Earth’s climate in
    mathematical terms, using specially designed computer programs. Due to the
    complexity of the software and the time periods modeled, climate models are typically
    run on mainframe computers.

    What do we know about models — not just climate models, but models in general?
    In a 1976 paper titled “Science and Statistics,” published in the Journal of the American
    Statistical Association, George E. P. Box wrote (My boldface for the next couple of
    quotes.):

    Since all models are wrong the scientist cannot obtain a “correct” one by
    excessive elaboration. On the contrary following William of Occam he should
    seek an economical description of natural phenomena. Just as the ability to
    devise simple but evocative models is the signature of the great scientist so
    overelaboration and overparameterization is often the mark of mediocrity.

    See this thread for some EXCELLENT comments about the GCMs’ code: http://wattsupwiththat.com/2013/07/27/another-uncertainty-for-climate-models-different-results-on-different-computers-using-the-same-code/

    Persevere through all of the over 200 comments; you’ll find gems like this one:

    “… the entire range of past variation is equally likely under their assumptions and procedures… .” [Brian H 12:22 PM 7/29/13 - edited emphasis]

    In a nutshell: they are junk.

    Note: Ed Ohugima did a back-of-the-napkin calculation last summer and was able to duplicate one projected temperature rise for the 21st century (it was 6 or 8 degrees F, I think) with a simple interpolation. This was further evidence that the IPCC’s models are super-expensive, gee-whiz, gadgets that do nothing more than a basic math calculator can do.

  159. I have NO IDEA how all those line ends/returns ended up in there! Hm, maybe “WordPress” is actually a climate model… .

  160. Pompous Git! I’ve missed you. How’s life down there on the island, now that spring has begun? Have those awful rains stopped yet? Hope all is well. J.

  161. Thanks for that witty and insightful essay, Pompous — too tired to read it with the care it deserved, but I got the gist and that was great. Like this esp.:

    “… we can always find a better fitting model. And then we’d still have to wait for new observations to check it.

    LOL.

  162. The Pompous Git:

    Your post at October 19, 2013 at 11:42 pm says

    Well worth reading Briggs’ little essay: The Data is the Data if you have not already done so:

    http://wmbriggs.com/blog/?p=6854

    Yes, that is worth reading, and in the context of this thread its most important statements are these

    Assume the regression is the best model of uncertainty. Is the “trend” causal? Does that regression line (or its parameters) cause the temperatures to go down? Surely not. Something physical causes the data to change: the model does not. There is no hidden, underlying forces which the model captures. The model is only of the data’s uncertainty, quantifying the chance the data takes certain values.

    But NOT the observed data. Just look at the line: it only goes though one data point. The gray envelope only contains half or fewer of the data points, not 95% of them. In fact, the model is SILENT on the actual, already observed data, which is why it makes no sense to plot a model on the data, when the data does not need this assistance. Since the model quantifies uncertainty, and there is no uncertainty in the observed values, the model is of no use to us. It can even be harmful if we, like many do, substitute the model for the data.

    The data is the data. But there is NO DATA for the future. The model is the prediction which we – n.b. we and not the data – make for the future. And we construct that model as a trend which can be extrapolated from the future.

    The model in the article is a linear trend. That is clearly not the correct trend model for global temperature time series: the most recent 17 years demonstrates that.

    A better trend model is provided by Akosofu. It is better because that trend predicted the very recent global temperature stasis. This link goes to discussion relating to it and shows it in graphical form.

    http://wattsupwiththat.com/2013/09/09/syun-akasofus-work-provokes-journal-resignation/

    Richard

  163. Janice Moore said @ October 19, 2013 at 11:53 pm

    Pompous Git! I’ve missed you. How’s life down there on the island, now that spring has begun? Have those awful rains stopped yet? Hope all is well. J.

    This is the worst spring for gardening since the one in the mid-80s when I started market gardening! We have had rain for nearly every day for the last month. Yesterday was fine and warm but the wind blew ~55km/hr gales. We do live in the Roaring Forties. Today was fine and warm, but the rain started again about an hour ago. So it goes…

  164. @ Richard Courtney

    I think Paul Coppin nailed the head on the hit. There’s an epistemological issue here. Belief versus knowledge. Most commenters in this thread have missed this. Briggs is certainly aware of it. I have yet to take Kip’s recommendation of visiting dot earth. I suspect it’s ugly over there.

    Yes, Akosofu’s model is very interesting. It will be even more interesting in 2020 when we will be able to view the temperatures of Earth over the next seven years with… 20 20 hindsight.

    While I agree Jaynes is an excellent read for statistics, I suggest Richard Taylor’s Metaphysics for those making “an unusually obstinate effort to think clearly”.

  165. The Pompous Git:

    Thankyou for your reply to me at October 20, 2013 at 1:01 am in which you say

    I think Paul Coppin nailed the head on the hit. There’s an epistemological issue here. Belief versus knowledge. Most commenters in this thread have missed this. Briggs is certainly aware of it.

    I am assuming you are referring to the post of Paul Coppin in this thread at October 18, 2013 at 5:27 am. In that post he writes

    What I do read in many posts, are rationalizations for beliefs that are based on the statistical analysis of past events.
    Probabilities are not certainties. Statistical analysis allows us to take past facts and develop beliefs about future facts, but it never allows us to actually know the facts – that only happens when the future is past, and we add the future facts to the dataset.
    Knowledge is always based on history. Knowledge of the future doesn’t exist. A belief about some aspect of the future is all there is. Statistical analysis and modelling provide us with degrees of comfort that our beliefs are, or are not, likely to happen, but the uncertainty is never completely quantifiable.

    There is an epistemological issue here but it is NOT the stark blackwhite issue of “Belief versus knowledge” which you and he assert. Few things divide that clearly (as I am well aware if only because of the activity I am to conduct in the next few minutes).

    Knowledge is what is known and can be justified by evidence.
    Belief is what the believer treats as being known because it is accepted on faith although it cannot be justified by evidence.

    Ideas and inferences exist between knowledge and belief. They include large amounts of uncertainty and doubt, but they enable us to operate in the real world.

    For example, I ‘know’ the light will come on in my hall when I turn on the hall light switch. No knowledge is completely certain and there were times when my knowledge was wrong because my hall light bulb had failed. But I ‘know’ the light will come on when I turn on the switch.

    Others do not share my knowledge of my hall light because they have never visited my home. Some of them may believe my hall light is operated by a switch because they trust my word on the matter. Others mat believe I do not have a hall light because they so distrust my word on anything that they accept my claim of a hall light ‘proves’ I don’t have one.

    Between these are people who accept that most houses in the UK have hall lights which are operated by a switch. So, these people accept as a useful and probable working hypothesis that my house has a hall light which is operated by a switch.

    Determining a trend in a time series data set is similar to accepting that my house has a hall light because most houses in the UK have hall lights which are operated by a switch. The trend assesses what is known to determine what is probable.

    Determining a trend and extrapolating that trend to obtain a prediction does not generate knowledge and it does not create belief: the determined trend provides a useful and probable working hypothesis about what will happen on the basis of what is known to have happened.

    The hypothesis may be wrong (e.g. because the trend is modelled as being linear when it is not) or may turn out to be wrong (e.g. because the behaviour changes from its past trend). But the future cannot be known. Determining a trend indicates what is likely to happen for at least the near term future.

    And now I must rush away to deal with issues of knowledge and beliefs but I will check back later today.

    Richard

  166. Even when we understand the processes involved in which result in certain trends, another danger exists when we do not apply proper constraints on those processes when modeled.
    I’m not going to try to outdo Mark Twain’s way of expaining the fallacy of extrapolating the future or the past without understanding the correct boundaries of the underlaying processes of a trend. It’s a very short read, and can very aptly be applied to the subject at hand:

    http://www.lhup.edu/~dsimanek/twain.htm

  167. “Hi, Alex S (at 7:11pm) — The intrinsic merit of Kip Hansen’s post aside, your equating his approach to the Fantasy Science Club’s CO2 conjecture is inaccurate.

    The climate models of the IPCC suffer far more from OVER-elaboration and needless complexity which, as some famous scientist I can’t remember (he’s quoted in Bob Tisdale’s book Climate Models Fail) said is: the sign of mediocrity”

    No, the so called complexity of IPCC is a claim you make due to the $$$ and hardware they spend and the fact they have to produce stuff around their dogma. They have no complexity in their dogma, they like the author put away many situations that make difficult to state their case.
    IPCC doesn’t show any complexity of a list of inputs might affect climate, more or less it is due to CO2 and “science” is settled.
    Imagine if IPCC would have included proper research about clouds, sun etc…
    Same here. The list of cases is enormous, and several have shown here examples of trends helping predict the future.
    The temptation to simplify with a binary thinking is the undoing of this text when there are several shades of grey.
    I wouldn’t have disagreed if it was simply stated that trend based prediction is usually an inferior technique to predict the future in most cases.
    But everyone seems to be after their own e=mc2
    Everyone is an aesthete.

  168. richardscourtney said @ October 20, 2013 at 2:04 am

    Determining a trend and extrapolating that trend to obtain a prediction does not generate knowledge and it does not create belief: the determined trend provides a useful and probable working hypothesis about what will happen on the basis of what is known to have happened.

    Richard, I must on this occasion disagree. Investment advisers, actuaries, and ever so many others rely upon their generating belief in the validity of their extrapolations. And we know this is so because those persuaded to entertain the validity of those beliefs hand over their hard-earned money to invest.

    This is however beside Kip and Briggs’ point: beware reification.

  169. The Pompous Git:

    Thankyou for your post at October 20, 2013 at 11:35 am in reply to my post at October 20, 2013 at 2:04 am

    OK. You say we disagree. Perhaps I am misunderstanding you because I do not see the disagreement.

    Several times in this thread – including in the post you say you disagree – I have said,
    “But the future cannot be known”.

    Any prediction is an assertion of what is likely to happen. Yes, some people believe predictions which may be the predictions extrapolated trends, or scientific model outputs or horoscopes, or etc.. But the believers’ beliefs do NOT mean the predictions are reality: the predictions are estimates of what is likely to happen.

    As I see it, and as I tried to explain, the predictions obtained from extrapolated trends are NOT what is known to happen: they are not knowledge because the future cannot be known. Some people choose to believe the predictions obtained from extrapolated trends – as some people choose to believe horoscopes – but that is the failing of the believers: it is not the fault of the extrapolated trends. The believers error is reification, and it is an error I am warning against making in my statement you have quoted in your post I am answering.

    The predictions obtained from extrapolated trends are an indication of the future based on what has happened in the past. As I explained, those predictions may be wrong but they are useful because trends continue until they don’t. Hence, the predictions give indications of the future which are better than chance, and doing better than chance is an advantage (as any casino manager will tell you).

    Richard

  170. Richard, you stated “Determining a trend and extrapolating that trend to obtain a prediction… does not create belief” and it is that portion of your statement with which I disagreed. I am perfectly happy to accept the Aristotelian account of knowledge as justified true belief. We agree that extrapolation into the future does not generate knowledge. But if that extrapolation does not generate belief, then what does it generate? You seem to be saying it generates “X” and people subsequently choose to believe, or not based on “X”. I am curious to understand what this “X” is.

  171. The Pompous Git:

    Thankyou!

    Your post at October 20, 2013 at 12:53 pm explains what I was not understanding. It says

    Richard, you stated “Determining a trend and extrapolating that trend to obtain a prediction… does not create belief” and it is that portion of your statement with which I disagreed. I am perfectly happy to accept the Aristotelian account of knowledge as justified true belief. We agree that extrapolation into the future does not generate knowledge. But if that extrapolation does not generate belief, then what does it generate? You seem to be saying it generates “X” and people subsequently choose to believe, or not based on “X”. I am curious to understand what this “X” is.

    Obviously, I was not clear, and it is hard to understand a reason for a disagreement when one thinks one has explained something adequately when one has not. Sorry.

    I defined what I understood to be belief and said – as you have quoted – that a prediction obtained from an extrapolated trend “does not create belief”. However, as you said and I agree, some people can choose to attach belief to a prediction (obtained from any method).

    I am saying the prediction does not ITSELF generate belief (any more than a chair does). But people can generate beliefs which they may attach to predictions from particular methods (similarly, a chair does not generate belief that it can support a person’s weight but a person may attach that belief to a chair they have yet to sit on).

    I will illustrate that with a classic con trick.
    The trickster sends four circulars to four sets each of 1000 people. The circulars contain predictions of e.g. stock market changes and the four circulars provide very different predictions. Each circular says to not send money but to wait for a future invitation to enable the recipients to join in an offered investment plan which would use the demonstrated investment scheme which uses an undisclosed formula. One of the groups would – by chance – have made much money by investing as described in its circular. The trickster makes no further contact with the other three groups. He divides the remaining group into four smaller groups each of 250 people, and repeats the process of sending circulars. Again, one of these smaller groups would – by chance – have made much money by investing as described in its circular. And those 250 people are the trickster’s target. Many of them now attach a belief to the claimed investment formula. The trickster sends each of them another circular but this asks each of the targets to invest $1,000 in the investment scheme. If 200 of those 250 people respond then the trickster obtains $200,000 for a scam which cost at most a few hundred $ to conduct. (Actually three rounds of fake predictions – not two – are usually used because people tend to be convinced by three successive successes.)

    In the illustration, the apparent success of the predictive investment formula induces the ‘marks’ to believe the formula works, but no such formula exists. The formula has not induced belief in its ability – it does not exist – but the desire to believe in the formula and, hence, its rewards induces belief in its ability.

    I hope that clarifies what I meant; i.e. predictions don’t create belief but people may.

    The prediction from an extrapolated trend provides an indication of the future which is better than a chance guess of the future. It is that improvement on chance which is what you call “X”.

    The prediction is not knowledge of what will or will not happen.
    And the prediction is not a belief that sometging will or will not happen.
    The prediction is an indication of what is more likely to happen than random chance would suggest.

    In principle this improvement on chance is similar to a weather forecast. Nobody believes a weather forecast is what will happen, and everybody knows a weather forecast is not knowledge of what will happen. But if the weather forecast is for rain then people tend to carry an umbrella.

    I hope I have now been more clear.

    Richard

  172. I believe I have come up with an example that may make what I believe Kip’s point more obvious.

    Let us suppose that the Tasmanian Education Department have hired me to test the IQ of the children at Franklin Primary School. Let us further suppose I record the following results:

    Alice has an IQ of 80
    Bertrand has an IQ of 100
    Clara has an IQ of 120

    Some here would seem to believe that Desmond’s IQ is more likely to be 140 than it is 100. I can assure you that having been schooled by that statistician to the stars, William/Matt/Briggs [delete whichever is inapplicable] that this is not the case. Fortune-tellers/necromancers/tree-huggers/numerologists/astrologers [delete whichever is inapplicable] will naturally disagree with me on this matter ;-)

  173. The Pompous Git:

    Sorry, but this time it is me that is bemused. I cannot understand why (in your post at October 20, 2013 at 1:41 pm ) anybody would think “Desmond’s IQ is more likely to be 140 than it is 100″.

    Richard

  174. Ted Carmichael said @ October 18, 2013 at 3:51 am

    The Theory of Gravity is the classic example of this. The empirical results are so thoroughly robust and understood that we even call it a Law. But we don’t know what “causes” gravity … we can model it very, very well, but we don’t know the cause. (Yes, there are a few hypotheses of late; but these are as yet uncertain.)

    There is no “The Theory of Gravity”; there are four theories of gravity with which I am familiar:

    Aristotle: “all bodies move towards their natural place. For some objects, Aristotle claimed the natural place to be the center of the Earth, wherefore they fall towards it. For other objects, the natural place is the heavenly spheres, wherefore gases, steam for example, moving away from the center of the Earth and towards Heaven and to the Moon.”

    Einstein: “General relativity, or the general theory of relativity, is the geometric theory of gravitation published by Albert Einstein in 1916.”

    Quantum theories: string theory and loop quantum gravity.

    You will notice that Newton does not appear on that list. Newton wrote: “Hypotheses non fingo”. (I do not make up hypotheses.)

    Newton’s Law is a statement that objects are attracted to each other in proportion to their mass. It appears to have been in operation for more than the last 10 billion years, thus predating Newton by a considerable margin. Theories of gravity are an attempt to explain the why of gravitation.

  175. richardscourtney said @ October 20, 2013 at 2:10 pm

    Sorry, but this time it is me that is bemused. I cannot understand why (in your post at October 20, 2013 at 1:41 pm) anybody would think “Desmond’s IQ is more likely to be 140 than it is 100″.

    Richard

    Me too, but clearly many do. I chose the example because I think the error that Kip is attempting to explicate is made more explicit. I could be wrong and stand to be corrected if that is the case.

  176. Another example that may make my (Kip’s?) point clearer. The Git and Mrs Git are members of an art buying group. We have a 1/25 share and each shareholder pays $1,000 per year over 10 years into the kitty. From that kitty, artworks are purchased and at the end of the 10 years, the artworks will be distributed by auction among the shareholders by auction. Artworks not desired by shareholders will be auctioned publicly and the money obtained distributed equally among the shareholders. Not that it’s relevant here, but we purchase only artwork by living Tasmanian artists.

    So, we will purchase $250,000 of artworks over a decade. Let’s assume we purchased three artworks in our first year for $10,000, $7,500 and $5,000 respectively and in that order. Extrapolation would indicate that our next purchase would be $2,500. But what we spend is dependent on aesthetics, how much remains in the kitty, whether an artist will hold a work until we accumulate sufficient funds to make the purchase, the availability of a suitable artwork to purchase and so on.

    Again, there is no suitable numerical model to base an extrapolation on. Yet extrapolation is not only possible, such extrapolations do occur in the real world that are based on the real data, as in the case that Briggs brought to our attention. As Briggs says (and he has said this before): “the data is the data”. The interpretation we put on the data is not data; it’s a product of our imagination.

    Hopefully that helps make the point clearer.

  177. Kip Hansen says:
    October 19, 2013 at 7:51 am

    Before using the apparent trend to make a prediction, once must know at least something about the system—the physical process itself—what we are talking about here!

    I think that is more or less what I stated here, and to which rgbatduke agreed:

    He would be on much more solid ground if he said that climate variables, particularly mean surface temperature anomaly, do not behave like such a sequence and, as a result, are not predictable to the desired level of accuracy using such a model. To the degree that your model fails to capture the dynamics of the actual process, statistics derived based upon that model are dubious, to say the least.

    Has our shared opponent been so defeated, debunked, and demoralized that we must find trivial issues to divide ourselves, because we are now locked in an aggressive, argumentative mode which must find some avenue for expression?

  178. Pompous Git: (Nice handle) You said, “Newton’s Law is a statement that objects are attracted to each other in proportion to their mass. It appears to have been in operation for more than the last 10 billion years, thus predating Newton by a considerable margin. Theories of gravity are an attempt to explain the why of gravitation.”

    The Theory of Gravity (or the Law of Gravity, if you prefer) is wholly an empirical model. What I meant by that is that it does not express a mechanism for gravity, only a (very well refined) description, based on copious amounts of data (measurements). Newton said it was not a hypothesis because he was convinced by the overwhelming consistency of observations. It is simple and elegant – one cause that explains a wide variety of observations. But the “cause” – that objects are attracted to each other in proportion to their mass – does not have a known underlying mechanism. In other words, we don’t know why objects are attracted to each other in proportion to their mass. We only know that it happens because we see it happen.

    Thank you for providing two examples of a spurious trend. (The artwork example and the IQ example.) I don’t think they prove your (or Kip’s) point however. In the IQ example we have additional information … we know that IQ is not likely to be related to the first letter in a student’s name. We also know that IQ forms a bell curve, and so is not likely to continue in an upward trajectory. In other words, we have two additional models of student IQ scores that we would bring to bear on that particular data set, and would thus probably ignore a linear trend as being spurious.

    For the artwork example, we also have a lot of additional information that would discount a linear trend. With Kip’s original BC example, we similarly have additional information, but nothing that would dissuade us from extrapolating a (weak) linear trend. And so a prediction of 110 buttons on the fifth day is – very weakly – the most likely outcome … it is more likely than any other number, based on the information we have. (You will recall that Kip’s point was that any prediction for day five is equally likely to occur. I disagree with the “equally” part.)

    It is useful, I think, to extend these examples, so that instead of just three data points there are, say, 100, or 5,000, or a million. Then it becomes obvious that a linear trend – one that perfectly matches every single data point – is a reasonable approximation for data point 101, 5001, or 1,000,001. This extension helps to show why the numbers, all by themselves, contain a non-zero amount of knowledge of whatever system produces them. Cheers.

  179. @ Ted Carmichael

    First, you are correct that my examples were flawed; perhaps I should wait until I have finished a litre of coffee before posting ;-) Nevertheless, the numbers without the additional information convey no information whatsoever. The coin toss example above should be sufficient to convey that.

    Apropos Law versus Theory, my view is what philosophers call The Received View; i.e. it is what is taught at the academy. You might find the following of interest:

    When Does a Theory Become a Law?

    This is something that comes up quite frequently in discussions between scientists and the general public. How much proof does it take for a theory to graduate to being a law?

    Law
    Because the words theory and law have such different meanings in the language of science, it is often a difficult question to answer, so instead, I’ll start by giving you a few similar questions to answer.

    How perfectly do you have to build a house so that it will become a single brick?
    How well do you have to write to change an entire dictionary into a single word?
    What would you have to do to change an entire symphony into a single note?

    If you are thinking that those questions don’t make much sense, then you are feeling very much like a scientist who has been asked “How much proof does it take for a theory to graduate to being a law?”

    http://thehappyscientist.com/study-unit/when-does-theory-become-law

  180. “fhhaynie says: October 19, 2013 at 12:50 pm

    For those who do not trust the use of statistics in data analysis, I suggest you read http://books.google.com/books?id=8C7pXhnqje4C&printsec=frontcover&source=gbs_ViewAPI#v=onepage&q&f=false. This is based on a paper I presented at an international corrosion symposium way back in 1971. It was published as a chapter in a handbook and has been revised and published twice since then. I think it is still valid. If you don’t understand how to use statistical techniques, you should talk to a statistician and at least learn from them. The probability that you will make mistakes in your research will be greater when you don’t use statistical techniques correctly. Much of the the IPPC research is a good example of missuse. Ask most any statistician.”

    FNHaynie:
    I hope we haven’t made it seem that we distrust statistics, perhaps better stated as disbelieve statistics. My perception is that most of us commenting here are convinced that statistics has immense value when dealing with data.
    Using RGB as an example, most of us have immense respect for presentations RGB has shared with us. Bluntly stated, I’d accept RGB’s findings easily without any unease. RGB’s handling of research is detailed with solidly understood data and metadata. Dissecting RGB’s articles is always educational and often illuminating. I’d be proud if any of my work had been equal in accuracy and efficiency.

    Separating out the bad CAGW statistics examples from proper statistics usage, CAGW’s bad examples are the ones that assert absolutism and insist on faith in the scientists rather than algorithm investigation.

    Our seeming disagreement mostly focused on when is a ‘prediction’ considered absolute or near absolute. Statisticians and many model code writers automatically keep in mind the confidence levels inherent to any run. Other experts reviewing the output also take confidence levels as a necessary part of the statistical output. They also take into account who the statistician is and oddly, the maturity level of the statistical model. Well written and proven models/modules/programs/code outputs are easily accepted. Modules written by an unknown just don’t get that level of acceptance until thoroughly reviewed.

    This is harder to define whom, so allow me to state; experienced statisticians allow the data to lead them to the analysis and often the deeper one delves the more value of specific trends is attained. Targeting of defined areas or problems just provides a data area focus for analysis.

    Contrast that with an advocate seeking to ‘prove’ anything. Advocates misuse data, metadata, filters, code and statistics to achieve their aims, not properly assess a ‘trend’. Their outputs are presented as the absolute ‘future’. A quick way to distinguish between the types is when trend issues are identified whether the owners accept corrections or seem deaf to all entreaties for correction/withdrawal.

    This are not unique problems specific to statistics, but are inherent to all presentations or sales pitches intent more on deceit than serious study.

    When those of us insisting on acknowledgement that trends are not absolute comment we’re definitely not denying the impressive and very extensive value of statistics. We’re trying to point out that even the finest statistics runs have confidence levels. If the confidence level equates to 100%, the trend probably does not need to be run; e.g., charting the sunrise.

    Excellent highly talented statisticians work to get their code and data to hit as high a confidence level as possible. They also review the runs to assess any causes for less than ideal conditions impacting the trend. This prepares them for questions about trend expectations; questions they are usually happy to answer honestly.

    The less than honest spin presenters are the ones blackening the statistics eye. They often use phrases like, “It’s a computer model”, or “Statistics is used to determine…” to inflate audience impressions of their product. They often start to sweat extensively and their eyes dart around when anyone asks specific technical about the model or statistics runs. Rarely do they answer technical questions with technical replies. Recently, over the last decade, their approach is to refuse direct answers but oddly tongue and mind twisting logic is later posted on the web as ‘official’ responses’ by third party individuals. A number of the most severely flawed usages of statistics where the owner ignores all non-worship comments and insists their models are validated. Falsifiable science is apparently suspended for their causes.

    I won’t claim to be a statistician nor that I love statistics. I have used statistics for trend models and spent many hours/days/weeks vetting data and statistic runs. Every time I knew that a data analysis program needed statistics to progress, it was amazing how much of my other work got done first. Diving into a pile of data with a statistics injection took more time for review than coding, by far. I can say I am impressed with the information inherent to the most innocuous boring mass of ordinary data and how statistics is the tool to achieve that information.

    • Thanks Theo,
      You have stated more cleary what I attempted to convey when I presented that paper years ago. The primary message was that it is easy to make mistakes in your research if you missuse statistical techniques because you don’t understand the math. Statisticians may not understand the physics but they do understand the math. Those that understand the physics (or think they do) but lack the understanding of the math, should consult with a statistician before designing their experiments or write code for a model to do what-if experiments with vertual reality.

  181. Statistics is simply another language form from the maths stable. There is no reason to trust or distrust it; but, as in the use of any language, it has it’s rules and if these are not obeyed, the outcome can be nonsense. Before digital computing became commonplace, we had to do the math by hand and consequently we knew what was what. Once calculator and big computing engines became fashionable, users did not need to know what was involved in the calculation and , I suspect , many may not know very much about statistics and when it is appropriate to use this or that statistical tool – hence GIGO. They forget that describing an event statistically cannot add new knowledge, any more than the operations of logic can. What it can do, if used prudently, is provide several views of the event in much the same way that an artist can produce several views of a still life by moving the source of illumination.
    It’s over 200 years since Hume demonstrated that correlation and causation are not necessarily related and that induction is a fragile tool. Indeed he envisaged that far from the universe being well-behaved and orderly, it might be manipulated by a mischievous devil out to tease us into false beliefs. But, heigh ho, it’s all we have. That’s why Science must be defended from hooligans such as those who profess to find validity for ‘post-normal’ science and ‘consensus’ validation.
    Science, based on inductive inference, has served us very well in many respects, but she is so so vulnerable.

  182. ” fhhaynie says: October 21, 2013 at 11:21 am

    …Those that understand the physics (or think they do) but lack the understanding of the math, should consult with a statistician before designing their experiments or write code for a model to do what-if experiments with vertual reality.”

    I agree with your comment and your latter sentence above I do agree with absolutely. If I could add a couple of words they would be, “…consult a statistician before and after. But I like your phrasing just fine.

    “Ted Carmichael says: October 21, 2013 at 12:13 pm

    @ATheoK: +1
    @The Pompous Git: I like the Theory/Law explanation. Thanks for posting it.”

    :-) Thank you.

    “mitigatedsceptic says: October 21, 2013 at 12:47 pm

    Statistics is simply another language form from the maths stable. There is no reason to trust or distrust it; but, as in the use of any language, it has it’s rules and if these are not obeyed, the outcome can be nonsense. Before digital computing became commonplace, we had to do the math by hand and consequently we knew what was what. Once calculator and big computing engines became fashionable, users did not need to know what was involved in the calculation and , I suspect , many may not know very much about statistics and when it is appropriate to use this or that statistical tool – hence GIGO. They forget that describing an event statistically cannot add new knowledge, any more than the operations of logic can. What it can do, if used prudently, is provide several views of the event in much the same way that an artist can produce several views of a still life by moving the source of illumination.
    It’s over 200 years since Hume demonstrated that correlation and causation are not necessarily related and that induction is a fragile tool. Indeed he envisaged that far from the universe being well-behaved and orderly, it might be manipulated by a mischievous devil out to tease us into false beliefs. But, heigh ho, it’s all we have. That’s why Science must be defended from hooligans such as those who profess to find validity for ‘post-normal’ science and ‘consensus’ validation.
    Science, based on inductive inference, has served us very well in many respects, but she is so so vulnerable.”

    “The Pompous Git says: October 21, 2013 at 1:11 pm

    “@ mitigatedsceptic…”

    That was rather well put :-)”

    I’m in complete agreement with you! Good statement @mitigatedsceptic! I flashed back to slide rules with your ‘math by hand’ phrase.

  183. Thank you.
    Yes, indeed, slide rules linear, circular and cylindrical. We also had a ghastly monster by NCR, I think, that looked and sounded like a cash register (Open all hours type of thing) and which printed out the calculations line by line. That really was a gem because it was so easy to trace back to find the (inevitable) errors.

  184. mitigatedsceptic says:
    October 21, 2013 at 12:47 pm

    They forget that describing an event statistically cannot add new knowledge ….

    That’s why Science must be defended from hooligans such as those who profess to find validity for ‘post-normal’ science and ‘consensus’ validation.
    —————

    Right you are. Now all one has to do is convince all of the proponents of CAGW of that fact.

  185. They forget that describing an event statistically cannot add new knowledge

    I am somewhat uncomfortable with this statement. New data, or “information” in the formal sense cannot be added, but knowledge about the event, in the sense of increased understanding or better interpretation, can be. Else, why use statistics at all?

  186. @Brian H Indeed, but I did say “What it can do, if used prudently, is provide several views of the event in much the same way that an artist can produce several views of a still life by moving the source of illumination.” That is the value of statistical conversations – they enable us to form different impressions of things and events.
    The real problems arise when statistical calculations are extended to make ‘projections’ into the future. We believe that everything is causally connected to everything else; but our models cannot embrace all possible causal relations including points (tipping points or bifurcation points) at which state changes take place.
    Newton warned us not to offer explanations that go beyond the observations, yet this is what the futurologists are up to. Science should be modest and admit to vast ignorance about future events

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