PS296 Chapter Notes - Chapter 19: Chi-Squared Distribution, Karl Pearson, Null Hypothesis
Document Summary
One classification variable: the chi-square goodness of fit test. Goodness of fit test is a test for comparing observed frequencies with theoretically predicted frequencies. Data are categorized along only one dimension (classification variable) The most common/important formula for the chi square statistic (x squared) involves a comparison of observed and expected frequencies. The expected frequencies are the frequencies you would expect if the null hypothesis were true. Summation is taken over both categories of response. If the null hypothesis is true, the observed and expected frequencies (o and e) would be reasonably close together and the numerator would be small, even after it is squared. How large the difference between o and e is also depends to some extent on how large a number we expected. Chi square was proposed by karl pearson and is often referred to as the pearson chi-square test.
