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> So you don't really need Bayesian logic to reason that you should test fewer people if you want a more significant result. That's misleading by the use of th
by jey 13y ago
> So you don't really need Bayesian logic to reason that you should test fewer people if you want a more significant result.
That's misleading by the use of the word "significant" which apparently means something in frequentism than it does in normal speech. I certainly wouldn't use "significant" in that way as a non-frequentist, I would instead rephrase what you said as:
> So you don't really need Bayesian logic to reason that you should test fewer people if you want more confirmation bias in your result.
And that's a statement I can definitely get behind!
- klodolph 13y agoThat's a good point. Conversely, when you run a test and get a result which is "not statistically significant", that doesn't mean that the measured effect is not significant. For example, if I test a drug and say "it does not cause cancer (p=0.17)", that's wrong. Yes, the correlation with cancer is not statistically significant, but it is significant in the normal sense of the word.
- _delirium 13y agoIt can definitely be used misleadingly, but it's not too out of line with normal scientific usage of the term. The "significant" in "significant figures" is the same: if a number has "11 significant figures", it doesn't mean the 11th digit is significant in the sense of being important or having a big impact, just that the 11th digit is within the measurement precision (as propagated through any subsequent calculations).