Resources

 

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Statistical Significance and the Dichotomization of Evidence

In light of recent concerns about reproducibility and replicability, the ASA issued a Statement on Statistical Significance and p-values aimed at those who are not primarily statisticians. While the ASA Statement notes that statistical significance …

Statistical significance testing and cumulative knowledge in psychology: implications for training researchers.

Data analysis methods in psychology still emphasize statistical significance testing, despite numerous articles demonstrating its severe deficiencies. It is now possible to use meta-analysis to show that reliance on significance testing retards the …

Statistical tests, p values, confidence intervals, and power: a guide to misinterpretations.

Misinterpretation and abuse of statistical tests, confidence intervals, and statistical power have been decried for decades, yet remain rampant. A key problem is that there are no interpretations of these concepts that are at once simple, intuitive, …

The Chrysalis Effect: How Ugly Initial Results Metamorphosize Into Beautiful Articles

The issue of a published literature not representative of the population of research is most often discussed in terms of entire studies being suppressed. However, alternative sources of publication bias are questionable research practices (QRPs) that …

The Extent and Consequences of P-Hacking in Science

A focus on novel, confirmatory, and statistically significant results leads to substantial bias in the scientific literature. One type of bias, known as "p-hacking," occurs when researchers collect or select data or statistical analyses until …

The N-Pact Factor: Evaluating the Quality of Empirical Journals with Respect to Sample Size and Statistical Power

The authors evaluate the quality of research reported in major journals in social-personality psychology by ranking those journals with respect to their N-pact Factors (NF)—the statistical power of the empirical studies they publish to detect typical …

The natural selection of bad science.

Poor research design and data analysis encourage false-positive findings. Such poor methods persist despite perennial calls for improvement, suggesting that they result from something more than just misunderstanding. The persistence of poor methods …

The pipeline project: Pre-publication independent replications of a single laboratory's research pipeline

This crowdsourced project introduces a collaborative approach to improving the reproducibility of scientific research, in which findings are replicated in qualified independent laboratories before (rather than after) they are published. Our goal is …

Tracking replicability as a method of post-publication open evaluation

Recent reports have suggested that many published results are unreliable. To increase the reliability and accuracy of published papers, multiple changes have been proposed, such as changes in statistical methods. We support such reforms. However, we …

Underreporting in Psychology Experiments: Evidence from a Study Registry.

Many scholars have raised concerns about the credibility of empirical findings in psychology, arguing that the proportion of false positives reported in the published literature dramatically exceeds the rate implied by standard significance levels. A …
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