Once we're at 99df for the Welch, we start to notice a small difference in p-value from the asympotic result, but since we're at 99d.f., we're not really in the 'consider it as converged to normal' region. If that's not what you want, you need to more carefully explain what you do want.Įxample with very different $n$: > x=rnorm(1e7,1.00001,1) This test can be either two-tailed or one-tailed contingent upon if we are testing that the two population means are different or if one is greater than the other. The p-values turn out to be the same to all the places shown in the second figure. This tool executes a two-sample student's t-Test on data sets from two independent populations with unequal variances. According to the F-Distribution Calculator, an F-value of 1.577 with numerator df n1-1 12 and denominator df n2-1 12 has a corresponding p-value of 0.22079. T = 0.9052, df = 14708415, p-value = 0.3654Īlternative hypothesis: true difference in means is not equal to 0 The Welch test will handle very large sample sizes. The only difference is in which table is used, and if the size of the smaller group is large enough, the tests will give almost identical p-values. The form of test statistic is the same in both cases. While you can compute the z-statistic, actually an ordinary Welch t-test will do that just fine - in R that's t.test with all its default options.
0 Comments
Leave a Reply. |