Importance Hacking in Science Practice

This chapter explores the concept of importance hacking in science practice, where researchers trick reviewers into thinking their findings are valuable or interesting. It provides an example of a paper on American politics that claims society is influenced by economic elites and interest groups, but upon closer examination, the model used only explains 7% of the variance. The chapter also discusses P curve analysis, a technique for analyzing research papers by examining the distribution of P values, and introduces the FIRE framework for making fast, irrelevant, repetitious, and evolutionary decisions.