The distribution of the p-values is also unequal on either side of 0.025 and 0.045 of the p-curve. The p-curve was right skewed, with an intriguing bi-modality. Two percent of the p-values were inaccurately reported as zero or ≥1.

The present study found the use of p-values in a total of 44,315 p-values published in 12,440 abstracts. The tacit assumption was that significant p-values reported were the result of publication bias. Bayesian binomial analysis was used to test the proportion of the p-values on either side of the 0.05 threshold (test for publication bias) or the 0.025 threshold (test for evidential value). A p-curve was generated from the p-values and used for analysis. Web scraping for abstracts published between 20 was done and all p-values extracted. The National Library of Medicine catalogue was searched for journals published in English, indexed in PubMed and tagged with dentistry Medical Subject Headings (MeSH) words. This was a descriptive and exploratory study that analysed the p-values published in oral health literature. The aim of this study was to assess publication bias and evidential value in oral health research. The p-curve, a distribution of p-values from a set of studies, is used to study aspects of statistical evidence in a scientific field.

Subsequently, statistically significant findings may be due to selective reporting rather than a true effect. The tendency to selectively report “significant” statistical results (file-drawers effect) or run selective analyses to achieve “significant” results (data-dredging) has been observed in many scientific fields.

Testing for assumptions data dredging