Talk:Statistical hypothesis test

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This article was the subject of a Wiki Education Foundation-supported course assignment, between 27 August 2021 and 19 December 2021. Further details are available on the course page. Student editor(s): Happyboi2489 (article contribs).

Common test statistics

I corrected the erroneous last test, ("regression t-test") to a correct F-test. Harald Lang, 2015-11-29.

Criticism

When used to detect whether a difference exists between groups, a paradox arises. As improvements are made to experimental design (e.g. increased precision of measurement and sample size), the test becomes more lenient. Unless one accepts the absurd assumption that all sources of noise in the data cancel out completely, the chance of finding statistical significance in either direction approaches 100%. However, this absurd assumption that the mean difference between two groups cannot be zero implies that the data cannot be independent and identically distributed (i.i.d.) because the expected difference between any two subgroups of i.i.d. random variates is zero; therefore, the i.i.d. assumption is also absurd.

This train of thought is unclear and does not make much sense to me. The contributor confuses two different assumptions, in addition to labeling them as "absurd" without any justification. 2607:FEA8:11E0:6C57:E9A6:1B95:A479:CA89 (talk) 16:49, 2 April 2020 (UTC)[reply]

The testing process

This section is not correct - or at best, is misleading - when the null is not simple or the test statistic is discrete. If (at step 6 of the first procedure) you select a significance level (like 5%) without regard to the available significance levels or (with non-simple nulls) if the true parameter is not at the significance level boundary then the probability of being in the rejection region will generally be lower than that selected level. On the other hand, with a discrete statistic if you *do* select it with regard to the available significance levels (which is pretty rare among what seems to be common practice) in that procedure then it's no longer consistent with the p-value approach unless *that too* is selected in like fashion (which in common practice is considerably more rare again) -- the first would be exactly at a selected level and the second would not. So either steps 6 and 8 of the first procedure have an issue or the claim of equivalence of the second procedure has an issue.

Glenbarnett (talk) 06:27, 17 June 2020 (UTC)[reply]

History

I looked at the inline cited article by Zarbell and in no way does it say that Fisher started as a Bayesian. Bayes is not mentioned at all. Bayesian was not really an school within statistics until the 1950s. That part of the sentence should be removed. Mcsmom (talk) 20:38, 8 December 2020 (UTC)[reply]