Resources

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 reliability and reproducibility of science are under scrutiny. However, a major cause of this lack of repeatability is not being considered: the wide sample-to-sample variability in the P value. We explain why P is fickle to discourage the …

For any given research area, one cannot tell how many studies have been conducted but never reported. The extreme view of the "file drawer problem" is that journals are filled with the 5% of the studies that show Type I errors, while the file drawers …

Recent controversies have questioned the quality of scientific practice in the field of psychology, but these concerns are often based on anecdotes and seemingly isolated cases. To gain a broader perspective, this article applies an objective test …

Data-dependent analysis—a “garden of forking paths”— explains why many statistically significant comparisons don't hold up.

A syllabi used for general linear model

Survey experiments have become a central methodology across the social sciences. Researchers can combine experiments’ causal power with the generalizability of population-based samples. Yet, due to the expense of population-based samples, much …

The GO FAIR Foundation's FAIR Capacity Building Programme provides professional and qualified training for data stewards who aspire to use methods developed under the Three-Point FAIRificaiton Framework (3PFF) in their daily work to make data and …

We present a simple mathematical technique that we call granularity-related inconsistency of means (GRIM) for verifying the summary statistics of research reports in psychology. This technique evaluates whether the reported means of integer data such …

Blogposts about psychology, reproducibility, replication etc.