Statistical Significance Filtering Overestimates Effects and Impedes Falsification: A Critique of.

ABSTRACT: Whether in meta-analysis or single experiments, selecting results based on statistical significance leads to overestimated effect sizes, impeding falsification. We critique a quantitative synthesis that used significance to score and select previously published effects for situation awareness-performance associations (Endsley, 2019). How much does selection using statistical significance quantitatively impact results in a meta-analytic context? We evaluate and compare results using significance-filtered effects versus analyses with all effects as-reported. Endsley reported high predictiveness scores and large positive mean correlations but used atypical methods: the hypothesis was used to select papers and effects. Papers were assigned the maximum predictiveness scores if they contained

SUBMITTER: Bakdash JZ 

PROVIDER: S-EPMC7783317 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature