Conceptual and Statistical Knowledge

 

A Power Primer

One possible reason for the continued neglect of statistical power analysis in research in the behavioral sciences is the inaccessibility of or difficulty with the standard material. A convenient, although not comprehensive, presentation of required …

Benefits of Open and High-Powered Research Outweigh Costs

Several researchers recently outlined unacknowledged costs of open science practices, arguing these costs may outweigh benefits and stifle discovery of novel findings. We scrutinize these researchers’ (a) statistical concern that heightened …

Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs

Effect sizes are the most important outcome of empirical studies. Most articles on effect sizes highlight their importance to communicate the practical significance of results. For scientists themselves, effect sizes are most useful because they …

Inappropriate fiddling with statistical analyses to obtain a desirable p-value: tests to detect its presence in published literature.

Much has been written regarding p-values below certain thresholds (most notably 0.05) denoting statistical significance and the tendency of such p-values to be more readily publishable in peer-reviewed journals. Intuition suggests that there may be a …

Power Analysis and Effect Size in Mixed Effects Models: A Tutorial.

In psychology, attempts to replicate published findings are less successful than expected. For properly powered studies replication rate should be around 80%, whereas in practice less than 40% of the studies selected from different areas of …

Safeguard Power as a Protection Against Imprecise Power Estimates

An essential first step in planning a confirmatory or a replication study is to determine the sample size necessary to draw statistically reliable inferences using power analysis. A key problem, however, is that what is available is the sample-size …

Statistical power and optimal design in experiments in which samples of participants respond to samples of stimuli

Researchers designing experiments in which a sample of participants responds to a sample of stimuli are faced with difficult questions about optimal study design. The conventional procedures of statistical power analysis fail to provide appropriate …

Too true to be bad: When sets of studies with significant and nonsignificant findings are probably true

Psychology journals rarely publish nonsignificant results. At the same time, it is often very unlikely (or “too good to be true”) that a set of studies yields exclusively significant results. Here, we use likelihood ratios to explain when sets of …

You Cannot Step Into the Same River Twice: When Power Analyses Are Optimistic

Statistical power depends on the size of the effect of interest. However, effect sizes are rarely fixed in psychological research: Study design choices, such as the operationalization of the dependent variable or the treatment manipulation, the …

A systematic review of statistical power in software engineering experiments.

Statistical power is an inherent part of empirical studies that employ significance testing and is essential for the planning of studies, for the interpretation of study results, and for the validity of study conclusions. This paper reports a …
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