Yes, you can form a CI for the odds ratio, but I would prefer to simply form CIs on the proportions. A more sophisticated way would be to run a log-linear model and then form CIs on the coefficients (or at least look at their standard errors).

The problem, though, is that most people don’t want to go to this much work. A p-value is convenient and simplistic, thus appealing, sad to say.

]]>I need to take a deeper look at your book to fully grasp what you mean, but I agree on the problem of p-value being used as “holy” value for everything, but i still think that in certain situation it is relevant, especially for “rejecting”.

The first example coming to my mind is independence test between population – Fisher exact test for example. When you are examining contingency table, and want to reject the null hypothesis of independence between two groups, how would you do that without testing? This has real world implication, especially in the medical field (for example the impact of smoking/having diabetes on heart attack frequency). I tend not to trust human judgement when it comes to analyzing “raw numbers”. (A CI for the odds ratio is also outputted – which I always report, but as stated above, it’s an “extension” of the p-value)

Thank you for your thoughts.

]]>wow!!! that deserves more than a comment. have you, or someone you regard, written a lengthy takedown of Type I and II?

but what might fit in a comment: if (and, of course, I accept the if) p-value/NHST is just another application of math’s proof by contradiction, then what’s the problem? if p-value/NHST isn’t such, why not?

]]>You and I don’t differ where you think we do. Actually, the difference in the way you and I view things is much more fundamental. You want some kind of Decision Thoery, whereas I want statistics to give me estimates of quantities and then I make my decision based on many factors of interest to me, an informal process.

]]>But I’m not quite sure I understand why having a large sample size specifically is a problem for the resulting p-values. Is it because the resulting p-value will have an exaggerated statistical significance thanks to such a large n, which could misrepresent its practical significance?

Or is it just generally because, even in cases where there’s a large sample, the type of hypothesis test a p-value represents doesn’t necessarily translate to meaningful conclusions about the population?

Sorry for the basic question!

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