Hypothesis testing

Hypothesis testing can be important when developing performance standards for your subordinates. Each of you will be a manager, and each of you will therefore need to develop performance standards which will clearly allow you (and your subordinate) to agree as to whether or not the subordinate met your requirement. You do not want to end a rating period with disagreement on the subordinate’s performance, and you do not want to lose a grievance to upper management should the subordinate appeal your performance rating. (A subordinate certainly has every right to appeal, but you should never lose an appeal!)

Suppose that you are manager of a restaurant, and need to develop a series of performance standards for each member of your staff. The restaurant is a full-service restaurant, with seated customers, menus, a hostess to meet customers and seat customers, etc. Examples might include Joe Theismann’s, an IHOP, an Outback Steakhouse, etc.

For this case study, develop one measurable, numeric performance standard for a member of the wait staff. Then show how you could take a random sample of 30 readings of the performance and develop a hypothesis test using the t statistical method to determine, at 95% confidence, if the performance standard was met. List 30 notional (hypothetical) “readings” and calculate a corresponding notional t-test to show how your subordinate hypothetically performed. You will need to calculate the sample mean, sample standard deviation, use the t table in the back of the book, etc., to complete the analytics. You must use Excel to log your data and perform many or all of your analytics, and you will submit your Excel file (and any other files you may find convenient to submit such as photos of your calculations if done long-hand). This may be a one-tail test.

At the 95% confidence level, using the notional data, can you conclude that the subordinate is meeting your standard.