Professor Chastain is interested in the "gender pay gap." This is the idea that women make less than men. However, a common complaint in response to this idea is that men and women work different jobs, and different jobs pay different amounts of money (so the gap is due to different jobs). To account for that, Professor Chastain collects data from 28 total jobs, looking at the salary for women and men for the SAME job (e.g., women engineers make $40,820, and men engineers make $55,432).

Profession	Women	Men
Engineering	40,820	55,432
Medical scientists	59,332	75,348
Software market research	52,104	65,988
... 28 total jobs	...	...

She gets a lot of numbers after running the statistics, including:

• t = 12.38
• p < 0.001
• women make $51,021.29 on average with a standard deviation of 15460.72
• men make $61,057.29 on average with a standard deviation of 17816.1
• Cohen's d is 2.34
• Plus, the df you found in the previous question

SHORT ANSWER (14 points): Using the template provided in the notes, how should she report and interpret the test results?