Comment on Say no to BAYES
Cattail@lemmy.world 2 weeks agoSo 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.
FishFace@piefed.social 2 weeks ago
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.
Cattail@lemmy.world 2 weeks ago
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
FishFace@piefed.social 2 weeks ago
I don’t understand exactly what you did with chatgpt but I wouldn’t trust it on this. A textbook or Wikipedia would be a better source.
In practice p-values are used with a normality assumption. That assumption is widely valid because of the central limit theorem which means that normal distributions show up very very often.
And in practice they’re used as a formula to decide when a result is “statistically significant” i.e to give an idea of how likely an observed difference is due to a real phenomenon. So if people in a drug trial report feeling ill for two fewer days on average, calculating the p value will answer the question “what are the chances there’s actually a difference?”
I’d look for more examples - loaded dice examples are usually easy to understand too.
Cattail@lemmy.world 2 weeks ago
I didn’t understand what chat gpt did entirely.
Ironically with loaded dice I would look distribution of results and see that it’s not uniform distribution after a billion tosses and say it’s not fair/ loaded. I would do that simply to avoid figuring out how to prove the probability of a given set of due results.
It’s more about the journey to the p value the calculate p value.
I have seen that 1 drug recovery time example and that’s the easiest given that it’s normal distribution and it can be put into a region that less than 5% probability