Different hypothesis tests make different assumptions about the distribution of the random variable being sampled in the data. These assumptions must be considered when choosing a test and when interpreting the results.
For example, the z-test (ztest) and the t-test (ttest) both assume that the data are independently sampled from a normal distribution. Statistics Toolbox™ functions are available for testing this assumption, such as chi2gof, jbtest, lillietest, and normplot.
Both the z-test and the t-test are relatively robust with respect to departures from this assumption, so long as the sample size n is large enough. Both tests compute a sample mean , which, by the Central Limit Theorem, has an approximately normal sampling distribution with mean equal to the population mean μ, regardless of the population distribution being sampled.
The difference between the z-test and the t-test is in the assumption of the standard deviation σ of the underlying normal distribution. A z-test assumes that σ is known; a t-test does not. As a result, a t-test must compute an estimate s of the standard deviation from the sample.
Test statistics for the z-test and the t-test are, respectively,
Under the null hypothesis that the population is distributed with mean μ, the z-statistic has a standard normal distribution, N(0,1). Under the same null hypothesis, the t-statistic has Student's t distribution with n – 1 degrees of freedom. For small sample sizes, Student's t distribution is flatter and wider than N(0,1), compensating for the decreased confidence in the estimate s. As sample size increases, however, Student's t distribution approaches the standard normal distribution, and the two tests become essentially equivalent.
Knowing the distribution of the test statistic under the null hypothesis allows for accurate calculation of p-values. Interpreting p-values in the context of the test assumptions allows for critical analysis of test results.
Assumptions underlying Statistics Toolbox hypothesis tests are given in the reference pages for implementing functions.