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

Research output: Contribution to journalArticle


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.


External organisations
  • University of Geneva
  • University of Hamburg
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

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


  • Bayes' theorem, Inferential statistics, Likelihood, Replication, Research program strategy, T-test, Wald criterion
Original languageEnglish
Article number460
JournalFrontiers in Psychology
Issue numberAPR
Publication statusPublished - 2018 Apr 24
Publication categoryResearch