Post hoc analysis that is conducted and interpreted without adequate consideration of this problem is sometimes called data dredging by critics because the statistical associations that it finds are often spurious.
[4] Post hoc analyses are not inherently bad or good;[5]: 12–13 rather, the main requirement for their ethical use is simply that their results not be mispresented as the original hypothesis.
[5]: 12–13 Modern editions of scientific manuals have clarified this point; for example, APA style now specifies that "hypotheses should now be stated in three groupings: preplanned–primary, preplanned–secondary, and exploratory (post hoc).
It would be poor practice for a data analyst to choose which of these tests to report based on which gave the desired result.
Sometimes the temptation to engage in post hoc analysis is motivated by a desire to produce positive results or see a project as successful.