![]() ![]() We discuss how this works in detail below. ![]() Frequently, you will calculate the expected values on the basis of the observed values, thereby generating your own model. ![]() The expected values either come from a model or from a reference speaker group. In contrast to the t-test, which requires the mean, the standard deviation, the sample size and, of course, normally distributed data, the chi-square test works with the differences between a set of observed values (O) and expected values (E). Perhaps the most versatile of these is the chi-square test. For these cases, we can use different significance tests that don’t assume a normal distribution. Often, however, our data is not normally distributed. In the last chapter, we introduced the t-test and saw that it relies crucially on the assumption that the data in our samples is normally distributed. The affordance of the chi-square test is that it allows us to evaluate data of which we know that it is not normally distributed. In this chapter, we discuss the chi-square test. ![]()
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