## checking if the residues are normal ad nauseum?

If I am checking whether my data fits a curve C1, I have to check to see whether the residues R1n are normally distributed, which is checking R1n against a normal curve C2, giving me residues R2m; which must be normally distributed, that is, must be checked against a normal curve C3, giving me residues R3p, and so on ad nauseum. Where does this end?

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 Recognitions: Homework Help Science Advisor The normal distribution of your residues is not necessary - if their mean is 0 and their standard deviation is 1, you are done. Any deviation from a normal distribution there would indicate some weird (non-gaussian) uncertainties for the individual data points.

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 Quote by nomadreid If I am checking whether my data fits a curve C1, I have to check to see whether the residues R1n are normally distributed, which is checking R1n against a normal curve C2, giving me residues R2m; which must be normally distributed, that is, must be checked against a normal curve C3, giving me residues R3p, and so on ad nauseum. Where does this end?
In the first place, what do you mean when you say you are "checking"? You aren't describing a definite statistical test. I can appreciate your general train of thought. If there were some method of determining whether a given sample definitely did or did-not come from a normal distribution then a similar method could be applied to residues of plotting the histogram of the data vs the normal probability density. Then a similar method could also be applied to residues of the residues etc. However, there is no such fool proof method. All standard statistical hypothesis tests for normality compute is the probablity of certain aspects of the observed data given than we assume it came from a normal distribution. If you don't assume it came from a given distribuiton, you can't compute anything. (If this is upsetting, see Bayesian statistics.)

It is possible that you could invent a statistical hypothesis test based on residues-of-residues. To compare the utility of that test to the customary tests, people would look a the "power" of your test. The "power" of a test is complicated to define. It isn't a single number. It is a curve or surface that depends on how you parameterize the shape of the non-normal distributions that you consider.

## checking if the residues are normal ad nauseum?

Thanks for the answers, mfb and Stephen Tashi. (Sorry for the delayed response.) Apparently statisticians rely quite a bit on "hm, looks OK". (I'm not at all a statistician, which you can certainly tell from my beginner's questions; I'm more used to those strange places in mathematics where correlation is a yes/no affair unless you are doing perturbation theory. On the other hand, prior assumptions are the heart and soul of mathematics: "Er, well, let's call (N, <) consistent, and have done with it.")
More seriously: the statistical test I had in mind for the beginning set of points was the Pearson's correlation coefficient or something similar, where the residues should (I think) be more or less normally distributed, because otherwise (it appears at first glance at the formula) one could construct some wild mismatch between data and a line yet come up with a high r2. It might even not be too difficult to construct such with a 0 mean and sd=1. But as was pointed out, such a counter-example would probably look weird. (Something like Anscombe's quartet.) Or, to a blind computer, there would be other tests (which I haven't got to yet in my self-study of statistics) to check if it was weird. But then I was not sure about a test for the following steps to check data (residues) against normality; your answers indicate that there is none. Interesting.

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