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Hypothesis-Testing Demands Trustworthy Data—A Simulation Approach to Inferential Statistics Advocating the Research Program Strategy

Published inFrontiers in Psychology, vol. 9, 14
Publication date2018
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.

Keywords
  • Bayes' theorem
  • Inferential statistics
  • Likelihood
  • Replication
  • Research program strategy
  • T-test
  • Wald
  • Criterion
Citation (ISO format)
KREFELD-SCHWALB, Antonia, WITTE, Erich H., ZENKER, Frank. Hypothesis-Testing Demands Trustworthy Data—A Simulation Approach to Inferential Statistics Advocating the Research Program Strategy. In: Frontiers in Psychology, 2018, vol. 9, p. 14. doi: 10.3389/fpsyg.2018.00460
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Article (Published version)
Identifiers
ISSN of the journal1664-1078
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