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Hypothesis testing: Fishers exact test and Binomial test Considering the population of girls with tastes disorders, I do a binomial test with number of success k = 7, number of trials n = 8, and probability of success p = 0 5, to test my null hypothesis H0 = "my cake tastes good for no more than 50% of the population of girls with taste disorders" In python I can run binomtest(7, 8, 0 5, alternative="greater") which gives the following result
How to resolve the ambiguity in the Boy or Girl paradox? There's no paradox here, just ambiguity We're giving a vague description of a "real life" situation, and you're supposed to turn that into a well defined probability space in which to express events and their probabilities This is often hard or impossible to do, with the only possible conclusion being "some assumptions are missing" In this "boy and girl" problem, there is additional
Learning probability bad reasoning. Conditional and unconditional . . . The other possibilities—two boys or two girls—have probabilities 1 4 and 1 4 a Suppose I ask him whether he has any boys, and he says yes What is the probability that one child is a girl? b Suppose instead that I happen to see one of his children run by, and it is a boy What is the probability that the other child is a girl? Now my
Expected number of ratio of girls vs boys birth - Cross Validated Thanks to the answers I now understand why the ratio would be 1:1, which originally sounds counter intuitive to me One of the reason for my disbelief and confusion is that, I know villages in China have the opposite problems of too high of boys:girls ratio I can see that realistically, couples won't be able to continue to procreate indefinitely until they get the gender of child they want
Sample notation: When to use capital $N$ vs lowercase $n$? In statistics and psychological research, what is denoted by capital N N vs lowercase n n? I work in psychological research and I've seen them used in two ways: Capital N N represents the entirety of our sample and lowercase n n are the groups in that sample e g We run an RCT with 100 100 participants, 50 50 in control and 50 50 in treatment, so N = 100 N = 100 and n = 50 n = 50 in each