AI Article Synopsis
- Researchers typically follow a two-step statistical process: conducting power analyses before collecting data and significance tests afterward.
- Critics argue that significance tests offer limited insights and can easily be misused in research.
- A proposed improved approach suggests replacing traditional power analysis with parameter estimation before data collection, and focusing on estimating probabilities of outcomes (better or worse) after data collection for more informative results.
Article Abstract
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. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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