Interesting piece on clinical trial sizes. "Small sample sizes often lead to chance findings, while large sample sizes are often statistically significant but not clinically relevant" Think about that...https://catalogofbias.org/biases/wrong-sample-size-bias/?utm_content=bufferaf20f&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer …
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>> Another way of viewing that might be; we can't prove stuff is safe/effective for lots of people to use, when we test it on lots of people? Hmmm..
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>. And I'm no statistician, but I think this bit.. "Be cautious in using p-values to support or disprove hypotheses, especially when a large number of statistical tests to provide the p-values have been done" ..might just mean 'cut the statistical bull***t out', in lay speak.
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