A one-sample hypothesis test is a statistical tool used to evaluate whether the mean of a single sample significantly differs from a known value, typically a population mean. In a school cafeteria context, this method can help assess performance issues, such as food quality and service speed. To conduct the test, one would formulate a null hypothesis (e.g., the average customer satisfaction rating is at least 4 out of 5) and an alternative hypothesis (e.g., the average rating is less than 4). This framework allows for a structured investigation into whether the cafeteria meets acceptable performance standards. Data collection is a crucial step in this process. For instance, a survey could be distributed to a random sample of students, asking them to rate their satisfaction with the cafeteria services on a scale of 1 to 5. Suppose the sample of 100 students yields an average satisfaction rating of 3.5 with a standard deviation of 0.9. Using this data, a one-sample t-test can be performed to calculate the t-statistic, which compares the sample mean to the hypothesized population mean. In this case, the calculated t-statistic would indicate how far the sample mean deviates from the expected mean under the null hypothesis. Upon calculating the t-statistic and comparing it to the critical value from the t-distribution, one can make a decision regarding the null hypothesis. If the t-statistic is significantly lower than the critical value, as in the example where the t-statistic