Five rules for good science (and how they can help you spot bad science)

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I have a few rules that I aim to use when I run studies. By considering what it looks like when these rules are inverted, they also may help guide you in thinking about which studies are not reliable. (1) Don't use a net with big holes to catch a small fish That means you should use a large enough sample size (e.g., number of study participants) to reliably detect whatever effects you're looking for! (2) Don't use calculus to help you assemble IKEA furniture  That means...
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Demystifying p-values

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There is a tremendous amount of confusion around what a p-value actually is, despite their widespread use in science. Here is my attempt to explain the concept of p-values concisely and clearly (including why they are useful and what often goes wrong with them). — What's a p-value? — If you run a study, then (all else equal, aside from rare edge cases) the lower the p-value, the lower the chance that your results are due to random chance or luck. More precisely: a p-value is the probab...
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