Statistics

Most scientific manuscripts include statistical analysis, and a study’s conclusions depend on the results of these analyses. If the data are analyzed or reported incorrectly, the manuscript will mislead readers. Therefore, as a scientist, and as a peer reviewer, it is important to have a solid understanding of statistics, and to carefully examine the statistical methods and reporting in manuscripts you review. If you do not feel qualified to fully evaluate the statistics, tell the editor this in your comments so that they know to ask someone else to review them.

Some questions to ask as you review statistical analyses and results are:

• Was the sample size appropriate and/or justified? Did the authors perform a power analysis as part of their study design?
• Did the data meet the assumptions of the tests used? (e.g., many statistical tests can only be used for data with a normal distribution. Data such as proportions or counts of the number of events are generally not normally distributed and have to be either transformed or, preferably, analyzed with statistical models suitable for these data types). Were the tests used appropriate?
• Are the individual data points statistically independent? If there were repeated measurements (for instance, multiple measurements on the same patient), have appropriate statistical models been used?
• Have potential sources of bias (e.g. confounding variables) been considered and accounted for in the analysis?
• When percentages are presented, are the numerator and denominator clear? E.g., “Of the 500 bee colonies, 200 (40%) were affected by the virus,” or, “Forty percent (200/500) of the bee colonies were affected by the virus.”
• Are p-values reported where appropriate? Generally, a p-value should accompany all statistical comparisons mentioned in the text, figures and tables. The actual p-value should be stated (e.g. p = 0.049 and p = 0.0021 rather than p ‹ 0.05 or p ‹ 0.01). However, it is acceptable to state p ‹ 0.0001 if the value is below this threshold. The Statistical Analysis section should also state the threshold for accepting significance, such as "Values of P ‹ 0.05 were considered statistically significant".