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I can't understand how a small effect can produce a significant result with a large sample? In short, this is because large samples offer us precise enough estimates to state that the parameter bein …

How long ago was the training set sampled? Whatever your studying may have a temporal component which left unmodelled will certainly lead to your model failing to internally validate. a) Z standard …

In what follows, please remember that I am showing an approach, not the approach. The approach depends on assumptions, much of which I can not validate (nor do I have the wish to validate thoroughly …

Your characterization of the p value is incorrect. For a two sided test, the correct interpretation of the p value is as follows: The p value represents the probability of observing a test statist …

Since precision and recall are basically binary outcomes, you could compare the precisions and recalls directly. However, with the given sample sizes I think you're probably going to have a poor time …

I'm not sure what resources you are following, but I fear they may not be very good ones. First, it appears that you're plotting the marginal distribution of the outcome. You may be doing this to det …

Your conclusion is/was correct. Randomization is not about balancing groups. Indeed, there is a good chance that unobserved confounders are unbalanced and yet randomization still works. In fact, t …

What you are doing is essentially shooting the side of a barn and then later drawing targets around your bullet holes so as to make it appear that you are a good marksman. Setting aside the fact that …

It may be possible to use Bayesian statistics here. Let's set testing aside for a moment and instead focus on estimation. If we can construct a model and assess the estimates of $A$, maybe we can say …

Surely, they can help me interpret the results of my test, one way or another? Power is something computed conditional on some assumption about the true effect size. If the effect size was at least …

This is a big topic, so I will provide highlights. It is true that statistics can not confirm a theory. However, we can say if data are consistent with some assumption about the world conditional on …

Does the statistically insignificant value from the post-hoc adjusted analysis supercede the initial analysis of the primary outcome which isn't adjusted for confounders? What I assume you're trying …

I probably wouldn't do this with a hypothesis test, but we can use some probabilistic reasoning here. Poisson regression is a way to sort of "fit" a distribution on various covariates (like day of wee …

My question is this: in a bayesian test of proportion with more than 2 variants, is there a pretty standard approach for evaluating the statistical significance of the difference between their posts? …

OK, first thing's first. Let's just do a test of proportions and see what we get N_A <- 1000 cure_A <- 200 N_B <- 300000 cure_B <- 51000 prop.test(c(cure_A, cure_B), c(N_A, N_B)) #> #> 2-sample te …