I have the following contingency table dat <- matrix(c(1,506,13714,878702),nr=2)
And I want to test if their is an association between events A:{a,not(a)} and B:{b,not(b)} | b | not(b) | --------+-----+--------+ a | 1 | 13714 | --------+-----+--------+ not(a) | 506 | 878702 | --------+-----+--------+ I am worried that prop.test and chisq.test are not valid given the low counts and low probabilites associated with 'sucess' in each category. Is it safe to use them, and what is the alternative? (given that fisher.test can't handle this data... hold the phone... I just found fisher.test can handle this data if the test is one-tailed and not two-tailed. I don't understand the difference between chisq.test, prop.test and fisher.test when the hybrid=1 option is used for the fisher.test. I was using the binomial distribution to test the 'extremity' of the observed data, but now I think I know why that is inapropriate, however, with the binomial (and its approximation) at least I know what I am doing. And I can do it in perl easily... Generally, how should I calculate fisher.test in perl (i.e. what are its principles). When is it safe to approximate fisher to chisq? I cannot get insight into this problem... How come if I do... dat <- matrix(c(50,60,100,100),nr=2) prop.test(dat)$p.value chisq.test(dat)$p.value fisher.test(dat)$p.value I get [1] 0.5173269 [1] 0.5173269 [1] 0.4771358 When I looked at the binomial distribution and the normal approximation thereof with similar counts I never had a p-value difference > 0.004 I am so fed up with this problem :( ______________________________________________ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html