Hypothesis-testing demands trustworthy data-a simulation approach to inferential statistics advocating the research program strategy

Antonia Krefeld-Schwalb, Erich H. Witte, Frank Zenker

Research output: Contribution to journalArticlepeer-review

2 Citations (SciVal)

Abstract

In psychology as elsewhere, the main statistical inference strategy to establish empirical effects is null-hypothesis significance testing (NHST). The recent failure to replicate allegedly well-established NHST-results, however, implies that such results lack sufficient statistical power, and thus feature unacceptably high error-rates. Using data-simulation to estimate the error-rates of NHST-results, we advocate the research program strategy (RPS) as a superior methodology. RPS integrates Frequentist with Bayesian inference elements, and leads from a preliminary discovery against a (random) H0-hypothesis to a statistical H1-verification. Not only do RPS-results feature significantly lower error-rates than NHST-results, RPS also addresses key-deficits of a "pure" Frequentist and a standard Bayesian approach. In particular, RPS aggregates underpowered results safely. RPS therefore provides a tool to regain the trust the discipline had lost during the ongoing replicability-crisis.

Original languageEnglish
Article number460
JournalFrontiers in Psychology
Volume9
Issue numberAPR
DOIs
Publication statusPublished - 2018 Apr 24

Subject classification (UKÄ)

  • Probability Theory and Statistics
  • Psychology (excluding Applied Psychology)

Keywords

  • Bayes' theorem
  • Inferential statistics
  • Likelihood
  • Replication
  • Research program strategy
  • T-test
  • Wald criterion

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