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Planning Falsifiable Confirmatory Research

📄 Original study
Kennedy, James E 2024 Current Era methodology

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Plain English Summary

Published in the APA's prestigious Psychological Methods, Kennedy lays down a challenge: if you want your psi study to actually prove something, you need to be able to show your idea is wrong, not just right. That's what "falsifiable" means -- designing experiments where failure is a real possibility. His framework combines several statistical tools and lands on a striking requirement: for the small effects typical in psi research, you'd need roughly 860 to 1,084 participants to run a properly powered study. That's a big ask! He also argues that simply claiming "any tiny effect counts" makes a hypothesis untestable, and that looking back at old studies to build your case is exploratory at best. Instead, researchers should plan ahead and spell out exactly what would count as success, failure, or an unclear result.

Abstract

Falsifiable research is a basic goal of science and is needed for science to be self-correcting. However, the methods for conducting falsifiable research are not widely known among psychological researchers. Describing the effect sizes that can be confidently investigated in confirmatory research is as important as describing the subject population. Power curves or operating characteristics provide this information and are needed for both frequentist and Bayesian analyses. These evaluations of inferential error rates indicate the performance (validity and reliability) of the planned statistical analysis. For meaningful, falsifiable research, the study plan should specify a minimum effect size that is the goal of the study. If any tiny effect, no matter how small, is considered meaningful evidence, the research is not falsifiable and often has negligible predictive value. Power ≥.95 for the minimum effect is optimal for confirmatory research and .90 is good. From a frequentist perspective, the statistical model for the alternative hypothesis in the power analysis can be used to obtain a p value that can reject the alternative hypothesis, analogous to rejecting the null hypothesis. However, confidence intervals generally provide more intuitive and more informative inferences than p values. The preregistration for falsifiable confirmatory research should include (a) criteria for evidence the alternative hypothesis is true, (b) criteria for evidence the alternative hypothesis is false, and (c) criteria for outcomes that will be inconclusive. Not all confirmatory studies are or need to be falsifiable.

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📋 Cite this paper
APA
Kennedy, James E (2024). Planning Falsifiable Confirmatory Research. Psychological Methods. https://doi.org/10.1037/met0000639
BibTeX
@article{kennedy_2024_falsifiable_research,
  title = {Planning Falsifiable Confirmatory Research},
  author = {Kennedy, James E},
  year = {2024},
  journal = {Psychological Methods},
  doi = {10.1037/met0000639},
}