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Recent events have led psychologists to acknowledge that the inherent uncertainty encapsulated in an inductive science is amplified by problematic research practices. In this article, we provide a practical introduction to recently developed statistical tools that can be used to deal with these uncertainties when performing and evaluating research. In Part 1, we discuss the importance of accurate and stable effect size estimates as well as how to design studies to reach a corridor of stability around effect size estimates. In Part 2, we explain how, given uncertain effect size estimates, well-powered studies can be designed with sequential analyses. In Part 3, we (a) explain what p values convey about the likelihood that an effect is true, (b) illustrate how the v statistic can be used to evaluate the accuracy of individual studies, and (c) show how the evidential value of multiple studies can be examined with a p-curve analysis. We end by discussing the consequences of incorporating our recommendations in terms of a reduced quantity, but increased quality, of the research output. We hope that the practical recommendations discussed in this article will provide researchers with the tools to make important steps toward a psychological science that allows researchers to differentiate among all possible truths on the basis of their likelihood.
Link to resource: https://doi.org/10.1177/1745691614528520
Type of resources: Primary Source, Reading, Paper
Education level(s): College / Upper Division (Undergraduates)
Primary user(s): Student
Subject area(s): Math & Statistics