Seven Steps Toward Transparency and Replicability in Psychological Science

Psychological scientists strive to advance understanding of how and why we animals do and think and feel as we do. This is difficult, in part because flukes of chance and measurement error obscure researchers’ perceptions. Many psychologists use inferential statistical tests to peer through the murk of chance and discern relationships between variables. Those tests are powerful tools, but they must be wielded with skill. Moreover, research reports must convey to readers a detailed and accurate understanding of how the data were obtained and analyzed. Research psychologists often fall short in those regards. This paper attempts to motivate and explain ways to enhance the transparency and replicability of psychological science. Specifically, I speak to how publication bias and p hacking contribute to effect-size exaggeration in the published literature, and how effect-size exaggeration contributes, in turn, to replication failures. Then I present seven steps toward addressing these problems: Telling the truth; upgrading statistical knowledge; standardizing aspects of research practices; documenting lab procedures in a lab manual; making materials, data, and analysis scripts transparent; addressing constraints on generality; and collaborating.