That would make it far too easy! You should try reading biology papers, they don't even put the numbers of rats (etc) in the methods section. One has to look for degrees of freedom in their figures and convert to sample size. Aaarrg!
Make it a habit never to read stuff with n < 100 for psychology. Reading studies that are statistically uninformative is bad use of your time could have spent reading a more informative study.https://twitter.com/annemscheel/status/986014565182959616 …
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I do a power analysis when I just have a vague idea for a new study
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I think people need to get serious about power analysis. 80% power is way too low to aim for. 95% at least. This enables easy interpretation of null findings because the high precision means that it will be an informative null, just a "maybe sampling error, lols" null.
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It's a rule of thumb. Most effect sizes in psychology are too small to be seen in n < 100 datasets. Those that are large enough are probably fake or due to data/analysis error. E.g., those studies with d = 2 for stereotype threat.
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