  P-values are widely used in both the social and natural sciences to quantify
the statistical significance of observed results. The recent surge of big data
research has made the p-value an even more popular tool to test the
significance of a study. However, substantial literature has been produced
critiquing how p-values are used and understood. In this paper we review this
recent critical literature, much of which is routed in the life sciences, and
consider its implications for social scientific research. We provide a coherent
picture of what the main criticisms are, and draw together and disambiguate
common themes. In particular, we explain how the False Discovery Rate is
calculated, and how this differs from a p-value. We also make explicit the
Bayesian nature of many recent criticisms, a dimension that is often
underplayed or ignored. We conclude by identifying practical steps to help
remediate some of the concerns identified. We recommend that (i) far lower
significance levels are used, such as $0.01$ or $0.001$, and (ii) p-values are
interpreted contextually, and situated within both the findings of the
individual study and the broader field of inquiry (through, for example,
meta-analyses).
