The interpretation of significance tests for independent and dependent samples.
The assumptions upon which a correct interpretation of the t-test depends are rarely fulfilled by data from the neurosciences. This applies to both independent and correlated samples. The Mann-Whitney U-test is suggested as an alternative for the t-test for independent samples. The way in which significant results from this test should be interpreted is discussed. The Wilcoxon matched-pairs signed-ranks test is not suggested as an alternative for the t-test for correlated samples, since significant results can occur with this test, even when there are no differences between the distributions of the two samples tested. A modification of the U-test for dependent samples is proposed instead. The use of the latter test, and of the U-test, is illustrated by numerical examples from real data.
D009482:Neurophysiology / Q000379:methods*; D011603:Psychophysiology / Q000379:methods*; D013223:Statistics as Topic / Q000379:methods*