The power or sensitivity of a binary hypothesis test is the probability that the test correctly rejects the null hypothesis (H0) when the alternative hypothesis (H1) is true. should this be addressed before every clinical studies?
it is not mandatory to check statistical power if you are working with big sample size. However, it is very helpful for finding the accurate sample size when you are working with small sample size. In clinical studies, it is very common to use power analysis.
It's not compulsory, but useful. Statistical power enables you to find the correct sample size. If the sample size is too small, the results may be invalid. If the sample size is too big, you may obtain false positive results i.e. they are significant purely because so many measurements were made.
it is not mandatory to check statistical power if you are working with big sample size. However, it is very helpful for finding the accurate sample size when you are working with small sample size. In clinical studies, it is very common to use power analysis.
In addition to Dr Pegman's response, you should be clear when designing your study what effect size you are seeking to detect. This should be defined as something that would be a clinically meaningful improvement and is derived from previous studies, expert knowledge, guidelines etc. not statistical theory. If you are happy to reject Ho at p
Thank you for the valuable inputs Andrew Paul McKenzie Pegman, Efehan Ulas, Huw Jones, and Eduard Gemdzhian Гемджян.
A priori power analysis is intended to avoid studies that can't address their primary question or studies that waste precious resources by being larger than they need to be. They also force you to define your primary question and think about clinically meaningful effect sizes. In that sense, I regard them as mandatory. As a result, I tend to reject grants or papers sent to me for review that don't include a sample size calculation. That said, most statisticians eschew a posteriori power calculations, since once the study is done, you either saw the effect or didn't. For the latter, it's a matter of taste and philosophy.
Dear Ed Gemdzhian Гемджян , who is right and why is he or she right according to you, please elaborate.
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Experts please share your opinion,
The power or sensitivity of a binary hypothesis test is the probability that the test correctly rejects the null hypothesis (H0) when the alternative hypothesis (H1) is true. should this be addressed before every clinical studies?
Is it always mandatory to check statistical power?
Regards
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