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

• Main Authors: Antonia Krefeld-Schwalb, Erich H. Witte, Frank Zenker
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2018-04-01
• Series: Frontiers in Psychology
• Subjects: Bayes' theorem; inferential statistics; likelihood; replication; research program strategy; t-test
• Online Access: http://journal.frontiersin.org/article/10.3389/fpsyg.2018.00460/full

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.