Post hoc analysis that is conducted and interpreted without adequate consideration of this problem is sometimes called data dredging (p-hacking) 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.