William Sealy Gosset, Ronald A. Fisher and “Student” were among the first to think rigorously about how much a statistical analyst’s confidence in his own conclusions ought to be reduced by the limited sample sizes he was forced to work with.
The issue for statisticians of the past was that data were expensive while potential explanatory factors were cheap. Today, the mirror image often reigns: Data are readily available, but honest explanatory factors can cost you your job thanks to political correctness.
~75 percent of the people are now in a protected group. This is a disaster for social science because you have to try to explain social problems without saying anything that casts any blame on any member of a protected group. And not just moral blame, but causal blame. None of these groups can have done anything that led to their victimization or marginalization.
To achieve political correctness, grant-hungry and tenure-hungry researchers slice and dice their way to statistically significant but temporary or even non-existent correlations.
These correlations do not survive replication of the experiment.