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Say no to BAYES
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this post was submitted on 18 Feb 2026
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Right. But that's what p-values quantify: given the number of trials and the observed variance and means, how likely is it that the two groups are drawn from the same distribution versus actually having different means?
So variance isn't "more important" than p-values; high variance means that (by definition) your p-value is lower (less confident) than it otherwise would be.
So I looked into the definition of P and it can depend on variance if you assume gaussian distribution.
I wouldn't know how you would get a P value for 2 different distribution with similar means. I can come up with the null hypothesis being that group a and group b are the same, but then idk how to relate that to a probability of given mean and variance of A is B.
In general you need to know the distribution in order to calculate p values, though there are statistical methods for deciding - with some confidence level - whether a sample conforms to some distribution.
I did ask chatgpt 5.2 how to calculate the p value the sets of means and variance and set the null hypothesis as the means being the same then used Pooled t-test. The ai determined that both samples were more than 13 than the p is less than 5%.
P value seems a concept with a mathematical descriptions, but then I run into a wall when it's like how do you figure out probably of group A having the values it has given group B values. I would need to see how people actually calculate their p values and null hypothesis to get concrete examples
I do like how the Wikipedia page shows that a set of 20 coin flips having 14 heads would have a p value above .05