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Larry Kaplan's avatar

A larger sample size can be the answer, but not always.

I 100% agree that increasing sample size increases confidence (by reducing the width of the confidence interval) which can change an inconclusive finding to a conclusive one; even when using the old school standard of p<0.05. This can be true to a fault. This is the issue of a study being over-powered. With a huge sample size, yes we can be very confident and most likely find statistical significance, but we still need to interpret the importance and utility of that result. A statistically significant result can still have very limited practical implications. This occurs when the effect size is still near zero even though statistical significance is found. This represents a different way to game the system. If you have enough money to buy enough trials, likely you'll be able to publish positive results even when those results really aren't going to matter. Granted, given the expense of medical trials this is a less likely scenario. I'm a social scientist working in education where there can exist very large secondary data sets that researchers use. Either way, like what everyone else is pointing to, the way conclusions are written in journal articles needs to be super clear. This should include not just statistical significance, but practical significance.

My 2 cents, thanks.

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John Perry's avatar

Thank you for writing this post in a way that any layperson can understand. Much appreciated!

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