Novel methods, research best practices, and tools for behavioral scientists.
From Bem's precognition paper to Many Labs and registered reports — a scholarly retrospective on the events that reshaped psychological science and what reform looks like in 2026.
DAGs, propensity scores, instrumental variables, regression discontinuity, difference-in-differences — a graduate-level tour of how to extract causal claims from observational data.
Why running 20 tests at α=.05 yields a 64% chance of a false positive — and what Bonferroni, Holm, FDR, and pre-registration actually do about it.
Students within schools, patients within hospitals, repeated measures within persons — when independence assumptions break and how to model what's actually there.
An honest, non-zealot guide. Bayesian inference shines for null evidence, sequential designs, and small samples — and isn't always the right answer.
The 0.80 convention came from somewhere. So did the underpowered-study problem. A practical guide to sample-size planning for t-tests, ANOVA, regression, and beyond.
Hedges' g, Glass's delta, omega-squared, Cliff's delta, NNT, CLES — a reference-quality tour of effect-size metrics and the field-specific benchmarks that replaced "small/medium/large."
Multiverse analysis shows you 200 results. CCA tells you what all 200 agree on. Here's how it works and why it matters for robust research.
Every model is a tradeoff between simplicity and accuracy. The Complexity Navigator maps that tradeoff and finds the sweet spot automatically.
What happens when you run both a p-value and a Bayes factor, then algorithmically synthesize the conclusions? A stronger, more nuanced finding.
You found a correlation. But is it real, or did your sample get lucky? SPF stress-tests your relationships across scales and resamples to find out.