PT  - JOURNAL ARTICLE
AU  - Michel, Martin C.
AU  - Murphy, T.J.
AU  - Motulsky, Harvey J.
TI  - New Author Guidelines for Displaying Data and Reporting Data Analysis and Statistical Methods in Experimental Biology
AID  - 10.1124/dmd.119.090027
DP  - 2020 Jan 01
TA  - Drug Metabolism and Disposition
PG  - 64--74
VI  - 48
IP  - 1
4099  - http://dmd.aspetjournals.org/content/48/1/64.short
4100  - http://dmd.aspetjournals.org/content/48/1/64.full
SO  - Drug Metab Dispos2020 Jan 01; 48
AB  - The American Society for Pharmacology and Experimental Therapeutics has revised the Instructions to Authors for Drug Metabolism and Disposition, Journal of Pharmacology and Experimental Therapeutics, and Molecular Pharmacology. These revisions relate to data analysis (including statistical analysis) and reporting but do not tell investigators how to design and perform their experiments. Their overall focus is on greater granularity in the description of what has been done and found. Key recommendations include the need to differentiate between preplanned, hypothesis-testing, and exploratory experiments or studies; explanations of whether key elements of study design, such as sample size and choice of specific statistical tests, had been specified before any data were obtained or adapted thereafter; and explanations of whether any outliers (data points or entire experiments) were eliminated and when the rules for doing so had been defined. Variability should be described by S.D. or interquartile range, and precision should be described by confidence intervals; S.E. should not be used. P values should be used sparingly; in most cases, reporting differences or ratios (effect sizes) with their confidence intervals will be preferred. Depiction of data in figures should provide as much granularity as possible, e.g., by replacing bar graphs with scatter plots wherever feasible and violin or box-and-whisker plots when not. This editorial explains the revisions and the underlying scientific rationale. We believe that these revised guidelines will lead to a less biased and more transparent reporting of research findings.