Error detection

Definition: Broadly refers to examining research data and manuscripts for mistakes or inconsistencies in reporting. Commonly discussed approaches include: checking inconsistencies in descriptive statistics (e.g. summary statistics that are not possible given the sample size and measure characteristics; Brown & Heathers, 2017; Heathers et al. 2018), inconsistencies in reported statistics (e.g. p-values that do not match the reported F statistics and accompanying degrees of freedom; Epskamp, & Nuijten, 2016; Nuijten et al. 2016), and image manipulation (Bik et al., 2016). Error detection is one motivation for data and analysis code to be openly available, so that peer review can confirm a manuscript’s findings, or if already published, the record can be corrected. Detected errors can result in corrections or retractions of published articles, though these actions are often delayed, long after erroneous findings have influenced and impacted further research.

Related terms: <a href='/glossary/research-integrity/'>Research integrity</a>, correction, retraction

References: Bik et al. (2016), Brown and Heathers (2017), Epskamp and Nuijten (2016), Heathers et al. (2018), Nuijten et al. (2016), &

Drafted and Reviewed by: William Ngiam, Ali H. Al-Hoorie, Jamie P. Cockcroft, Dominik Kiersz, Sam Parsons, Suzanne L. K. Stewart, Marta Topor

Note that we are currently working on an automated mechanism to link references cited above with their full-length version that can be found at with all references used so far.