# Ten Points for High-Quality Statistical Reporting and Data Presentation
## 4 Discussion
A recent survey found that only 23% of the top biomedical journals reported that they routinely employed statistical review for all original research articles [51]. Introduction of specialist statisticians to the peer review process has made peer review more specialized. In addition, statistical reviewing is time intensive, limited by both reviewer supply and expense. In biomedical journals, there is no single model for statistical review in peer review strategies [51; 52; 53]. Some journals recruit statistical methodologies to the editorial board, some draw their statistical reviewers from an external pool. If all papers cannot be statistically reviewed, editors have to select which manuscripts should undergo statistical scrutiny. There are also models where subject reviewers are assisted to comment on the statistical aspects of a manuscript [47; 48; 54]. However, these checklists cover extensively all aspects of data analysis and are not straightforward for non-statistical reviewers to get an overall impression of the statistical quality. Some form of simplified checklist could be handy for editors and reviewers to spot the issues that might indicate more serious problems in the reporting of scientific articles. I hope that editors and reviewers could use the short quality test proposed in this paper for deciding when the presentation in a manuscript is clearly inadequate and they should recommend rejecting the manuscript. I agree with [PERSON] and [PERSON] [48] that if the reviewer cannot find the basic information and description related to the data analysis, the reviewer does not need to read the whole article. After checking tables and figures and reading through the statistical analysis subsection in the methods section, the reviewer can reject the manuscript on good grounds. When the proposed simple quality test shows that the statistical reporting and data presentation are appropriate, the whole article needs to be read and further reviewed. In recent years, several journals have tried to improve peer review processes [55]. Their efforts have been focused on introducing openness and transparency to the models of peer review [56]. New strategies in peer review might help to address persistent statistical reporting and data presentation issues in the medical literature [55]. Software algorithms and scanners have been developed to assess internal consistency and validity of statistical tests in academic writing [57]. However, their use is still rare and limited to flag specific potential errors. The open peer review, where all peer reviews are made openly available brings into use new models where the quality of a paper may be assessed by the whole scientific community. The pre- and post-publication peer review models include commenting systems for the readership. Readers could use tools, such as proposed in this paper, to give feedback to authors. Subsequently, authors prepare a second version of the manuscript reflecting the comments and suggestions proposed by the scientific community. My validation set of articles included four journals with different levels of prestige and visibility. The reporting of data analysis information was more detailed and useful for the reader in the more visible journals (_Lancet_ and _JAMA Psychiatry_). This is in line with previous studies [5; 6]. High-impact journals have more rigorous review process, including extensive statistical reviews. Several researchers in scientific communication have recommended sending out all manuscripts with numerical data for statistical review [8; 58; 59]. In addition, the strict monitoring of revisions made in manuscripts is important. The proposed quality test is short. It includes only nine items. These items measure the data presentation quality and the description of data analysis methods. It does not include items about study design, justification of sample size, potential sources of bias, common statistical methodological issues, or interpretation of results.