The chi squared goodness of fit test is typically conducted as a one-tailed test. This test assesses whether the observed frequency distribution of a categorical variable differs significantly from an expected frequency distribution. Specifically, it evaluates the null hypothesis that the observed data follows a specific distribution against the alternative hypothesis that it does not.
In this context, the chi squared statistic computes the discrepancy between observed and expected frequencies, with the test focusing on whether this discrepancy is significant enough to reject the null hypothesis. Since we are only interested in determining if the observed distribution is significantly different from the expected distribution in one direction (i.e., whether the discrepancy is larger than what would be expected by chance), it is effectively treated as a one-tailed test.
However, it’s essential to note that some situations might call for a two-tailed approach, especially if researchers are interested in deviations in both directions. Yet, as a standard procedure, the chi squared goodness of fit test is commonly referred to as a one-tailed test for practical purposes in hypothesis testing.