Final answer:
A goodness of fit test is always conducted as a right-tailed test to compare observed values with expected values in a specified distribution using the chi-square statistics.
Step-by-step explanation:
A goodness of fit test is always conducted as a right-tailed test. This statistical test is used to determine whether a set of observed values matches the expected values in a particular distribution. For the goodness of fit test, the null hypothesis (H0) asserts that the population fits a specific distribution, while the alternative hypothesis (Ha) contends that the population does not fit that distribution. The chi-square distribution is commonly used for calculating the test statistic.
College students might use a goodness of fit test to evaluate whether there is a discrepancy between actual data and theoretical models in various contexts, such as the distribution of college majors among graduates or local AP exam results compared to a national ethnic distribution.
It's important that each expected value in the observation or category be at least five to meet the Chi-square test validity requirement. The test statistic is compared to the Chi-square distribution to decide if there is a significant difference between observed and expected frequencies.