The goodness-of-fit test establishes whether an observed frequency distribution mirrors a claimed distribution. Consider the dataset of people visiting the gym on weekdays. One can perform a goodness-of-fit test to determine whether the observed client attendance agrees with the expected frequency of client attendance. To perform a goodness-of-fit test, the dataset values must be randomly selected and have a frequency value for each category, with the expected frequency of each category being at least 5. The chi-square test statistic for the goodness-of-fit test can be computed using the shown formula. Here, O and E represent the observed and expected attendances, k is the number of weekdays, and n is the number of sample values or attendance counts recorded. The number of degrees of freedom is k minus one. Goodness-of-fit hypothesis tests are always right-tailed, implying that the critical region and critical values are located at the extreme right of the distribution curve. The critical values and P-values help determine if there is a good fit between the observed and expected values.