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Ok, now I'm confused, because I was under the impression that this is the first thing I ever learned in statistics - that a p value is how likely it is that a v
by dmd 13y ago
Ok, now I'm confused, because I was under the impression that this is the first thing I ever learned in statistics - that a p value is how likely it is that a value created by chance would have been as extreme as the one the experiment gave. Is this wrong?
- trthatcher 13y agoYou have the right idea. You're assuming an underlying model for the data. You have a test statistic( that estimates a model parameter) and you have a hypothesis regarding a parameter. The p-value is the probability that you get a test statistic more extreme than the one observed assuming that your hypothesis is true. Ex. You have a sample of 1000 men's heights. You compute the sample average height as 5'9 and a sample standard deviation of 3 inches. (Unlikely) hypothesis: the average height is 4 feet. Your p-value is the probability of getting an sample average more extreme than 5'9 given that your 4 ft height hypothesis is true. Given that the sample standard deviation is 3 inches and 5'9 is 7 standard deviations from 4ft... the p-value is going to be small, so you'll reject that. Note: I'm leaving out details and assumptions
- dmd 13y agoYeah, that's what I was taught. So what are people answering instead?
- tel 13y agoPeople can be easily lead to misinterpret p-values even if they can define them. Most often people assume that p values indicate something about the correctness of a model or an inference. This is the classic p(d|h) v p(h|d) debate.
- tel 13y agoIt's not wrong, but it did open up some great questions as to what it means to have values created by chance and how that relates to "this experiment".