Email me. our 2-way discussion is inappropriate on someone else’s blog.

After reading STATS 101, I feel like Cotton Mather after he was told everything he learned about witches (statistics) is nonsense. Bummer. Five semesters of statistics courses and now I need to be re-educated in modern statistics.

Regards,
Ray

Thank you for your really nice polite reply. It is not what I get over at IOD, desmogblog, Gavin’s Garage, and many other sites in the Kingdom of the Warmers.

Data is always limited especially for many lab and field experiments with animals and plants, and most of us doing bioassays and experiments with them don’t have the time and resources to do these with a large number of reps, but use t-test and reviewers of our papers on insect pheromone research don’t object.

When testing decadal means for sample interval of a month, I using 300 numbers for each decade, and that would seem quite sufficient for a t-test. I actually never got around to checking if the distribution was normal.

Although many consider a shotgun an inelegant weapon, Craig Venter used it on the human genome project and it got the job done. Presumably, he was in a real hurry so he could use the results to restore the hair of his youth and make billions with a sure-fire, gene-based cure for male baldness.

My rejection of global warming and climate change is not based upon the usual of climate metrics but on being alive for 64 years. Prior to this year, the climate in Metro Vancouver has been more or less the same although the weather has been quite variable and strongly influenced by the ENSO. However, the winter of 1991-92 was an exception and was quite cold (like 0 to ca -10 deg C for 2 months) due to the Mt Pinatubo eruption.

Starting this spring, my body sensors are detecting a much different type of air. It has a coolness that I never experienced and it feels really creepy.

BTW: Did you check JD’s and AM’s websites using the posted links. What is your assesment of them. Over at IOD John D is called a crackpot and most don’t even know about AM.

Let me take a stab at your question because Matt is busy writing a book and I am busy wasting time. T-tests assume a normal (Gaussian) distribution (the old bell-shaped curve) which never exists in actual data (only in theoretical distributions of an infinity of averages). Since the (limited) temp data is not normally distributed, the t-test is inappropriate and gives misleading results.

Blasting data with a shotgun is inelegant. Your flat-out rejection of GW hypotheses may or may not be correct, but the t-test doesn’t add any supporting evidence one way or another.

Which is a conclusion one might draw from Matt’s Chapter 1, but I have confidence will be better explained in the future Chapters.

PS Gauss was still a remarkable genius. I admire him, anyway.

(not including t-tests, which are mostly useless).

You didn’t answer my question. And why did you say these t-tests were applied indiscriminately?

If I want to determine if the mean Tmax and Tmin temps for each month of 1900 are the same or different than that of 2000, what stat test(s) would you recommend?

I’d pull out the 12 gauge pump shotgun (aka simple unpaired t-test) and blast the data with 0-0 buckshot (cf The Getaway) because it gets the job done and leaves no survivors.

Here is a good stat problem. Imagine an experiment with two rats, Andy and Barney. You feed Andy cat food and Barney dog food. Then Barney dies. What is the probability that dog food is poison to rats?

Hint: there is both a frequentist and Bayesian answer to this problem, although both border on logical absurdity. One would really like to try the dog food out on more than one rat, just to add a little “confidence.” Also, it may surprise people how many allegedly scientific experiments are carried out with only two rats, i.e. two sampling units.

I am no fan of t-tests, especially when applied indiscriminately as they were in that table.

I’ll talk about t-tests in my Chapter 10.

Never saw Daly’s web site before. I see he is dead.

Chapter 2 is just about ready.

http://www.usefulinfo.co.uk/climate_change_global_warming.php

and locate the large table with the t-tests. Could you explain to me if this method of stat analysis of the CET records is OK or not OK?

I’m using this same method of stat analysis of temp records from several very remote weather stations. When I post my results over at Tim Lambert’s blog, everybody just dumps all over me and call me names like stupid, fool, idiot etc.

This is because I flatout reject any and all hypotheses that the activaties of man have any influence on climate whatsoever, and they do not like me at all especially Bernard J, Barton Paul, Dano, etc.

So far my results show no evidence of any global warming at this site, have detected the PDO shift from a cool to warm phase and back to a present cool phase quite accurately, and show that climate at this site is the same for 2000-06 interval as it was for the 1900-06 interval. Indeed I have found constant mean Tmax and Tmin intervals of 60-70 years at this site.

I would like to post some result here for you to check out. However I don’t how the data will display and you don’t have a preview button so I can check check this.

Have you checked out the late John Daly’s webite at: http://www.John-Daly.com. What is wrong his work? Everybody over at Tim’s IOD call him names also. Check out some the temp-time plots in the “Station Temperature Data” especially Death Valley.

Now we’re getting somewhere (and I don’t see you as flippant).

You are interested in the probability of statement A—which is usually called an event in classical statistics—given evidence E.

In frequentist statistics, you cannot say what Pr(A|E) is. At least, you cannot say so until you have re-run the “experiment” that would or would not let A obtain an infinite number of times.

The relative frequency of times A obtains in that infinite sequence—and only in that infinite sequence—is defined to be the probability of A.

If A is a statement, or event, that cannot happen an infinite number of times (for example A = “Mrs Clinton wins the Presidency in 2008″) then you are out of luck in classical probability. You just cannot answer the question. A has no probability.

Another example: A = “A star will show on page 12 one day hence” where E = “There is a notebook with 52 pages, and today on just one I will draw a star, and three days hence I will destroy the notebook.” The logical probability of A given E is 1/52. But you are not allowed to say so in frequentist statistics. (The only reason to emphasize “destroy the notebook” is to highlight this is a unique event;; it is not needed.)

True, A is just a map, but it is an exact one. You will look at the notebook at page 12, and either a star will be there or it won’t.

Another example due to Stove:: A = “Bob is a horse” given E = “Bob is a winged horse.” The logical probability of A given E is 1, but the argument schema, of course, has no relative frequency.

The reason to bring up an argument schema, is that frequentists are always obliged to posit an infinite series of experiments, and so must convert your concrete argument A and E into an argument schema where other A’s and E’s—identical in every way except for the substitution of labels—are assumed to exist. How else can you get to infinity?

So in the example above, you might like to say A_2, A_3, … are future notebooks which you will force me to either write a star on page 12 or not. I’ll certainly get a cramp as I get near drawing in an infinite number of notebooks.

Actually, I can see I am going to far. I think I will make this a posting for itself. This is just the kind of thing, incidentally, that’s going into the “Stats 601″ book, although with more detail there.