Inadequate sample sizes are kryptonite to good science–they produce waste, spurious results, and inflated effect sizes.  Doing science with an inadequate sample is worse than doing nothing.  In the neurosciences, large-scale surveys of the literature show that inadequate sample sizes …

Sizing up behavioral neuroscience – a meta-analysis of the fear-conditioning literature Read more »

[UPDATE: Thanks to twitter I came across this marvelous book(Jussim, 2012) that does a great job explaining the Pygmalion effect, the controversy around it, and the overall state of research on expectancy effects.  I’ve amended parts of this post based on …

We’ve Been Here Before: The Replication Crisis over the Pygmalion Effect Read more »

The New Statistics emphasizes effect sizes, confidence intervals, meta-analysis, and Open Science.  There’s a lot of momentum to adopt this change of focus.  For example, the APA recently released new guidelines for reporting quantitative research and throughout it emphasizes reporting …

Why effect sizes? A tutorial (especially for Neuroscientists) Read more »

The replication crisis isn’t just about sample size and statistical inference.  Another key issue is measurement: the process of turning observations into quantitative statements about our sample.  It’s tricky.  In many cases we’ve run before we learned to walk, adopting methods …

The complexity of measuring a ‘simple’ behavior (novelty preference tests seem terrible) Read more »