OK, I've sobered up so I'm going to give you a fictitious example of how Simpson's paradox can incorrectly skew research conclusions.
Let's say at the University of Cucks, with a student body of 3,000, they offer three majors: aerospace engineering, political science, and basket weaving. Here's the breakdown of the number of students studying each major by gender.
| Male | Femoid | Total |
Aerospace Engineering | 600 | 200 | 800 |
Political Science | 500 | 500 | 1,000 |
Basket Weaving | 400 | 800 | 1,200 |
Total | 1,500 | 1,500 | 3,000 |
Straightforward, right? OK, let's move on. Here's the average score, out of 100, broken down by major and gender:
| Male | Femoid | Average of all students in major |
Aerospace Engineering | 69 | 63 | 67.5 |
Political Science | 76 | 74 | 75 |
Basket Weaving | 93 | 90 | 91 |
Average of all students by gender | 77.73 | 81.07 | |
Interesting, right?
You see, at the hypothetical University of Cucks, the average femoid student has a higher score than the average male student. An inexperienced researcher might be tempted to conclude that therefore, femoid students outperform male ones.
But the average male student has a higher score than the average female student in every major. This occurs because a larger proportion of femoids are studying the easiest major (basket weaving), and there are fewer of them studying the hardest one (aerospace engineering), skewing the aggregate means.
Simpson's paradox essentially describes the phenomenon in which a trend appears in different subgroups of a dataset which disappears or even reverses when these groups are analyzed as a whole.
I'm not sure if Simpson's paradox applies to your argument IRL but this is why I am generally skeptical when someone says "femoids have a higher GPA or graduation rate" in order to conclude that "femoids perform better academically".
Yeah