Key Principles in Ethical Data Analysis and Handling Conflicts of Interest
This chapter covers ethical issues in data analysis, such as p-hacking (massaging data until significant results emerge) and HARKing (hypothesizing after the results are known). The chapter also discusses conflicts of interest, including financial conflicts as well as unreasonable ambition, egotism, “publish or perish” pressures that academics face, and pressures to secure grant funding. Recommendations are provided for avoiding and managing these challenges. Having multiple people conducting analyses is suggested as one way to maximize the likelihood of ethical data analysis. The chapter also provides recommendations for journal editors, department and university administrators, and funding agencies for ensuring that they do not inadvertently incentivize unethical data analytic practices.