Zoltan Dienes

TL;DR: The default P-value threshold for statistical significance is proposed to be changed from 0.05 to 0.005 for claims of new discoveries in order to reduce uncertainty in the number of discoveries.

TL;DR: It is argued Bayes factors allow theory to be linked to data in a way that overcomes the weaknesses of the other approaches, and provides a coherent approach to determining whether non-significant results support a null hypothesis over a theory, or whether the data are just insensitive.

TL;DR: This article proposed to change the default P-value threshold for statistical significance for claims of new discoveries from 0.05 to 0.005, which is the threshold used in this paper.

TL;DR: This article presents some common situations in which Bayesian and orthodox approaches to significance testing come to different conclusions; the reader is shown how to apply Bayesian inference in practice, using free online software, to allow more coherent inferences from data.

TL;DR: These distinctions are discussed in their relationship to similar distinctions such as procedural-declarative, conscious-unconscious, verbalizable-nonverbalizable, direct-indirect tests, and automatic-voluntary control and an outline of how these distinctions can be used to integrate and relate the often divergent uses of the implicit-explicit distinction in different research areas are illustrated.