Improving Inferential Analyses Predata and Postdata

David Trafimow

doi:10.1037/met0000697

Improving Inferential Analyses Predata and Postdata

2024-09-09

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AI

DOI：10.1037/met0000697

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The standard statistical procedure for researchers comprises a two-step process. Before data collection, researchers perform power analyses, and after data collection, they perform significance tests. Many have proffered arguments that significance tests are unsound, but that issue will not be rehashed here. It is sufficient that even for aficionados, there is the usual disclaimer that null hypothesis significance tests provide extremely limited information, thereby rendering them vulnerable to misuse. There is a much better postdata option that provides a higher grade of useful information. Based on work by Trafimow and his colleagues (for a review, see Trafimow, 2023a), it is possible to estimate probabilities of being better off or worse off, by varying degrees, depending on whether one gets the treatment or not. In turn, if the postdata goal switches from significance testing to a concern with probabilistic advantages or disadvantages, an implication is that the predata goal ought to switch accordingly. The a priori procedure, with its focus on parameter estimation, should replace conventional power analysis as a predata procedure. Therefore, the new two-step procedure should be the a priori procedure predata and estimations of probabilities of being better off, or worse off, to varying degrees, postdata.

Keyword:

a priori procedure

sample size

gain-probability analyses

skew normal distributions

delta log skew normal distributions

Improving Inferential Analyses Predata and Postdata

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