(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
Four years ago, in a post entitled, “How can statisticians help psychologists do their research better?”, I gave the following recommendations to researchers:
– Analyze all your data.
– Present all your comparisons.
– Make your data public.
And, for journal editors, I wrote, “if a paper is nothing special, you don’t have to publish it in your flagship journal.”
Changes since then?
The above advice is fine, but it’s missing something—a big something—regarding design and data collection. So let me two more tips, arguably the most important pieces of advice of all:
– Put in the effort to take accurate measurements. Without accurate measurements (low bias, low variance, and a large enough sample size), you’re drawing dead. That’s what happened with the beauty-and-sex-ratio study, the ovulation-and-clothing study, the ovulation-and-voting study, the fat arms study, etc etc. All the analysis and shared data and preregistration in the world won’t save you if your data don’t closely address the questions you’re trying to answer.
– Do within-person comparisons where possible; that is, cross-over designs. Don’t worry about poisoning the well; that’s the least of your worries. Generally it’s much more important to get those direct comparisons.
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