Part One – Hypothesis Testing Read Lecture Four . Lecture Four starts out with the five-step procedure for hypothesis testing. What is this? What does it do for us? Why do we need to follow these...

Part One – Hypothesis Testing

Read Lecture Four
. Lecture Four starts out with the five-step procedure for hypothesis testing. What is this? What does it do for us? Why do we need to follow these steps in making a judgement about the populations our samples came from? What are the “tricky” parts of developing appropriate hypotheses to test? What examples can you suggest where this process might be appropriate in your personal or professional lives? (This should be started on Day 1.)

Part Two – T-tests

Read Lecture Five
. Lecture Five illustrates several t-tests on the data set. What conclusions can you draw from these tests about our research question on equal pay for equal work? What is missing from these results to give us a complete answer to the question? Why? (This should be started on Day 3.)

Part Three – F-test

Read Lecture Six
. Lecture Six introduces you to the F-test for variance equality. Last week, we discussed how adding a variation measure to reports of means was a smart thing to do. Why does variation make our analysis of the equal pay for equal work question more complicated? What causes of variation impact salary that we have not discussed yet? How can you relate this issue to measures used in your personal or professional lives? (This should be completed by Day 5.)

Your responses should be separated in the initial post, addressing each part individually, similar to what you see here