 “P hacking” refers to a set of practices which include: checking the statistical significance of results before deciding whether to collect more data; stopping data collection early because results have reached statistical significance; deciding whether to exclude data points (e.g., outliers) only after checking the impact on statistical significance and not reporting the impact of the data exclusion; adjusting statistical models, for instance by including or excluding covariates based on the resulting strength of the main effect of interest; and rounding of a p value to meet a statistical significance threshold (e.g., presenting 0.053 as P &lt; .05). “Cherry picking” includes failing to report dependent or response variables or relationships that did not reach statistical significance or other threshold and/or failing to report conditions or treatments that did not reach statistical significance or other threshold. “HARKing” (Hypothesising After Results are Known) includes presenting ad hoc and/or unexpected findings as though they had been predicted all along (Kerr 1998); and presenting exploratory work as though it was confirmatory hypothesis testing (Wagenmakers et al. 2012). Five of the most widespread QRPs are listed below in Table 1 (from Fraser et al. 2018), with associated survey measures of prevalence.