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We conducted this scoping review of common errors identified by editors and reviewers of biomedical manuscripts. Errors includes items that a reviewer or editor might identify as needing correction. The errors were categorized by section of the manuscript: Introduction, Methods, Results, Discussion, and References. After screening 87 published studies, 16 papers were selected for data extraction. Of these 16 studies, the most frequently represented disciplines were Medicine (n = 5), Radiology (n = 2), and Psychiatry (n = 2). The most reported common errors included inappropriate study design, inadequate sample size, poor statistical analysis, and unclear and inadequate description of methods. Abstracts not reflecting the content of the paper were the most frequent general common error in biomedical manuscripts. The findings of this study offer one perspective on common errors in biomedical manuscripts and might be a useful guide for novice authors. [ABSTRACT FROM AUTHOR]

Statistics are a quintessential part of scientific manuscripts. Few journals are free of statistics-related errors. Errors can occur in data reporting and presentation, choosing the appropriate or the most powerful statistical test, misinterpretation or overinterpretations of statistics, and ignoring tests of normality. Statistical software used, one-tailed versus two-tailed tests, and exclusion or inclusion of outliers can all influence outcomes and should be explicitly mentioned. This review presents the corresponding nonparametric tests for common parametric tests, popular misinterpretations of the P value, and usual nuances in data reporting. The importance of distinguishing clinical significance from statistical significance using confidence intervals, number needed to treat, and minimal clinically important difference is highlighted. The problem of multiple comparisons may lead to false interpretations, especially in p-hacking when nonsignificant comparisons are concealed. The review also touches upon a few advanced topics such as heteroscedasticity and multicollinearity in multivariate analyses. Journals have various strategies to minimize inaccuracies, but it is invaluable for authors and reviewers to have good concepts of statistics. Furthermore, it is imperative for the reader to understand these concepts to properly interpret studies and judge the validity of the conclusions independently.

Statistics are a quintessential part of scientific manuscripts. Few journals are free of statistics-related errors. Errors can occur in data reporting and presentation, choosing the appropriate or the most powerful statistical test, misinterpretation or overinterpretations of statistics, and ignoring tests of normality. Statistical software used, one-tailed versus two-tailed tests, and exclusion or inclusion of outliers can all influence outcomes and should be explicitly mentioned. This review presents the corresponding nonparametric tests for common parametric tests, popular misinterpretations of the P value, and usual nuances in data reporting. The importance of distinguishing clinical significance from statistical significance using confidence intervals, number needed to treat, and minimal clinically important difference is highlighted. The problem of multiple comparisons may lead to false interpretations, especially in p-hacking when nonsignificant comparisons are concealed. The review also touches upon a few advanced topics such as heteroscedasticity and multicollinearity in multivariate analyses. Journals have various strategies to minimize inaccuracies, but it is invaluable for authors and reviewers to have good concepts of statistics. Furthermore, it is imperative for the reader to understand these concepts to properly interpret studies and judge the validity of the conclusions independently. [ABSTRACT FROM AUTHOR]