Identifying the Appropriate Test-Statistic and Describing the Basic Steps of Testing of Hypothesis

Module 20 is about identifying appropriate test statistics and understanding the basic steps of hypothesis testing. Objectives include identifying the correct test statistic when population variance is known or unknown, and describing the steps in statistical hypothesis testing.

The six basic steps are: formulating null and alternative hypotheses, identifying the level of significance and appropriate test statistic, determining the critical value (tabular value), computing the Z or T values, deciding whether to accept or reject the null hypothesis, and stating the conclusion.

A Z-test is used for testing the mean of a population, comparing means of two populations with large samples (sample size n ≥ 30), or testing proportions. An example involves comparing average teaching salaries of men vs. women.

A T-test is used for testing the mean of one population or comparing means of two populations when the population standard deviation is unknown and the sample size is small (n < 30). An example is measuring average salaries of professors from certain universities with a small sample.

An F-test is used to compare two or more population variances, regardless of sample size, and is the basis of ANOVA. It also compares means of two or more independent groups and looks at interaction effects between variables. An example includes comparing the variability of scores from three sections of students.

The video provides several examples. For instance, a scenario with 20 calamansi trees uses a T-test because n < 30. A sample of 70 observations uses a Z-test because n > 30. A survey of 28 teachers uses a T-test (n < 30). Comparing three salesmen's monthly sales uses an F-test (two or more populations). A sample of 500 residents with household pets uses a Z-test (n > 30).

For attendance, students are asked to differentiate between the Z-test, F-test, and T-test and submit their answers on the GCR under the connect section.