John Clements <email@example.com>
This package provides both Welch’s T-Test and the more well-known Student’s T-Test.
These functions both make Bayesians unhappy, and with some reason. Specifically, these functions try to answer this question: "if these two sets are chosen from the same distribution, what is the likelihood of an outcome this strange or stranger?" The actual question that people typically want to answer is instead: "given these two sets of observations, what’s the likelihood that the two sets are chosen from the same distribution?" The problem is that this Bayesian reversal is possible only with some assumption about the prior likelihood of the various distributions, which is generally not known. For this reason, experimenters often just tell the Bayesians to go away and let them run their t-tests.
Separately, there are problems with the classic "Student’s T-Test" proposed by Gosset. Specifically, its results are valid only if both samples are known to be drawn from distributions that are normal, and that have the same variance. In the case that the two variances are not known to be equal, the correct choice is apparently to use Welch’s t-test, which does not make this assumption. Unsurprisingly, it has less power, but apparently not hugely less.
A note about implementation: the interesting part of the computation is determining the cumulative distribution function (CDF) of the appropriate distribution. This library simply uses the built-in "incomplete beta function" beta-inc from the math library to perform this computation. So really, Neil Toronto did all of the heavy lifting, here.
|(require t-test)||package: t-test|
(welch-t-test S1 S2 [ #:t-statistic t-statistic?]) → Real S1 : (Sequenceof Real) S2 : (Sequenceof Real) t-statistic? : Boolean = #f
The two-tailed p-value represents the likelihood of an occurrence this strange or stranger given the null hypothesis that the two distributions have the same mean.
Let’s have an example. Suppose that the there are two sections of a class, and in the first one, the final scores are given by s1, and the second section’s scores by s2:
(define s1 '(63.9 92.3 80.9 85.9 86.8 87.6 91.7 84.4)) (define s2'(86.6 91.3 63.7 69.2 78.5 74.0 85.4 89.0))
We want to test whether there’s a significant difference between the two. First, we choose a p-value, perhaps 0.02, representing a two percent chance. Then, we run the test:
(welch-t-test s1 s2) ; => 0.3630116587607044
That is, there’s about a 36% chance of something this strange or stranger occuring. This is way above our threshold of 2%, so we conclude that there’s insufficient evidence to reject the null hypothesis.
(student-t-test S1 S2 [ #:t-statistic t-statistic?]) → Real S1 : (Sequenceof Real) S2 : (Sequenceof Real) t-statistic? : Boolean = #f
This function uses the "Student’s t-test", rather than Welch’s t-test.
Many thanks to Tim Brown for posting his translation of the C code for Welch’s t-test to Rosetta Code.