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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
Expected number of ratio of girls vs boys birth - Cross Validated Expected girls from one couple$ {}=0 5\cdot1 + 0 25\cdot1 =0 75$ Expected boys from one couple$ {}=0 25\cdot1 + 0 25\cdot2 =0 75$ 1 As I said this works for any reasonable rule that could exist in the real world An unreasonable rule would be one in which the expected children per couple was infinite
How to resolve the ambiguity in the Boy or Girl paradox? 1st 2nd boy girl boy seen boy boy boy seen girl boy The net effect is that even if I don't know which one is definitely a boy, the other child can only be a girl or a boy and that is always and only a 1 2 probability (ignoring any biological weighting that girls may represent 51% of births or whatever the reality is)
probability - What is the expected number of children until having the . . . A couple decides to keep having children until they have the same number of boys and girls, and then stop Assume they never have twins, that the "trials" are independent with probability 1 2 of a boy, and that they are fertile enough to keep producing children indefinitely
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 group
what is the difference between a two-sample t-test and a paired t-test When you use a paired T-test, you are essentially doing a one-sample test, where your one sample consists of the paired differences between outcomes in two groups If you create a new sample of these difference values and then apply the formula for a one-sample T-test, you will see that this is equivalent to the paired test
should I use log or raw data in non parametric tests? I would like to run wilcoxon rank sum test to see if there are differences between boys and girls in each age group in regards to antibody levels Should I continue to use log10 or raw values?