There are situations where all intents to be cool, calm and polite is impossible . . . The event is the jeremiad a lot of Psychologists (and other people) against the NHST (null hypotheses significance tests) used to, seemly restlessly, to exercise. This time we are dealing with ?How Do We Do Hypotheses Testing? Author: J. Gill. artsci.wustl.edu/~jgill/papers/hypos.pdf
At the paper´s final conclusive 3 section
Not sufficiently educated think that:
__1__The NHST´s intends to make sure the Null Hypotheses H0 is true (or untrue). NOT AT ALL: they simply measure its plausibility through the p-value: it is completely impossible to get certainly from events/procedures ruled by chance. In this context p-value has an immutable meaning: by our (arbitrary) criterion to separate, by alpha, plausible from intolerable risk not to reject H0: if we get p-value < alpha do suggests (the word was carefully chosen) that H0 is probably untrue. It worth to note that, under H0 true, the set of p-values are uniformly distributed in [0, 1]. Other current criticism is that p-value does not inform how large is the difference between the observed value of the parameter and the one from which the null is unlike to be taken as true. This point revels/suggests a serious fail in what concerns statistical tests Psychologists education. The general formula for the test of a parameter X can be written: _______ V = (X - (X0 + d))/ s By X0 is expressed the value relative to the null hypotheses, s the standard deviation of the differences on X, and d is the net difference between the null and X. Once the sample distribution of V is established, one easily can obtain a Confidence Interval for d. It seems that a lot of people know is that d doesn´t exist, I mean.