Suppose a clinical psychologist working at a veteran’s hospital has been treating patients for depression for over 30 years. During this time, he has consistently measured their levels of depression at the end of their treatment on a well-established, widely-used, valid, and reliable scale. He knows based on various research already published using this depression scale that most depressed patients score an average of 150 on the scale . He also knows that the scale is scored to indicate levels of the severity of a patient’s depression. Specifically, patients who score less than 125 tend to experience few depression symptoms, if any. Patients who score 126-141 tend to experience slight depression symptoms; those who score 142-157 experience moderate symptoms of depression, and those who score 158 or more tend to experience severe, prolonged depressive symptoms.

Assuming his patients are a representative sample of the population, he compared his former clients’ scores (M = 147, SD = 35, N = 1180) at the end of their treatment to the scores of depressed patients in the general population. He wanted to use this information to support the effectiveness of his treatment and charge more for his services.

Review, compute and report in APA style a one-sample t-test to evaluate his presumed treatment effect. Calculate the effect size for this effect. Interpret the results 1) in terms of their statistical significance, 2) in terms of the impact his treatment has made on his patients’ lives, and 3) in terms of his likelihood of getting a pay raise based on these results.

To support your decision regarding his pay raise, explain the difference between statistical significance and importance. Explain the need for both a hypothesis test to examine potential treatment effects and the need for an effect size calculation.