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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?
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)
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
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