Many non-parametric test statistics, such as U statistics, are approximately normal for large enough sample sizes, and hence are often performed as Z-tests. Z-tests are employed whenever it can be argued that a test statistic follows a normal distribution under the null hypothesis of interest.
See for example Hypothesis Testing: Two-Sample Inference - Estimation of.
Although there is no simple, universal rule stating how large the sample size must be to use a Z-test, simulation can give a good idea as to whether a Z-test is appropriate in a given situation. Reference: The calculations are the customary ones based on normal distributions. Our null hypothesis is that the mean body fat for men and women is equal. The two-sided test is what we want (Prob > t).
#T two tailed hypothesis test calculator software
The software shows results for a two-sided test and for one-sided tests. When using a Z-test for maximum likelihood estimates, it is important to be aware that the normal approximation may be poor if the sample size is not sufficiently large. The results for the two-sample t-test that assumes equal variances are the same as our calculations earlier. If the population variance is unknown (and therefore has to be estimated from the sample itself) and the sample size is not large ( n μ 0, it is upper/right-tailed (one tailed).įor Null hypothesis H 0: μ=μ 0 vs alternative hypothesis H 1: μ≠μ 0, it is two-tailed. Therefore, many statistical tests can be conveniently performed as approximate Z-tests if the sample size is large or the population variance is known. left-tailed, right-tailed or two-tailed.
Because of the central limit theorem, many test statistics are approximately normally distributed for large samples. The F-test calculator for testing two population variances makes it easy to calculate the test statistic, F critical value and the p value given the sample information, level of significance and the type of alternative hypothesis (i.e.