Well, the way research is usually run they look for "statistical significance" and try to have "power" of ~80% to find it.[1] The power is controlled by the sam

Well, the way research is usually run they look for "statistical significance" and try to have "power" of ~80% to find it.[1] The power is controlled by the sample size, which is a big determinant of how much is spent on the study so it is chosen to be the lowest acceptable value. Anyway, my point is if there is an 80% chance of getting a result and you run two trials with those same odds, then only about 64% of the studies will replicate with the correct result (in R): p = 0.8 res = t(replicate(1e5, sample(0:1, 2, prob = c(1 - p, p), replace = T))) # 0 = Replicated wrong result # 1 = Non-replicated result # 2 = Replicated correct result > table(rowSums(res)) 0 1 2 3944 32135 63921 So, the standard practice is designed to generate a large percentage of conflicting results. [1] 80% is the target, but is probably usually optimistic