A critical aspect of the statistical review involves evaluating the appropriateness of the study design. The statistician should assess whether the chosen design is suitable for addressing the research hypothesis and whether it aligns with the study’s objectives. The methodology should be sound, with considerations for potential biases and confounders. Additionally, the manuscript should include sample size and power calculations to ensure that the study is adequately powered to detect the expected effects. The statistician should confirm the accuracy of these calculations and assess whether they have been transparently reported. In randomized trials, it is essential to examine the process of randomization and ensure that it is adequately described. Any blinding procedures should also be clearly explained and justified, as these are crucial for minimizing bias.

Proper data handling is vital for the credibility of the research findings. The statistician should scrutinize how data were collected, managed, and cleaned. This includes evaluating how missing data were handled, how outliers were treated, and whether any data transformations were performed. It is important that these processes are reported transparently so that others can understand and potentially replicate the study. Additionally, the statistician should ensure that assumptions underlying statistical tests—such as normality, homoscedasticity, and independence—are checked and reported. If these assumptions are not met, alternative methods or adjustments should be applied, and this should be clearly communicated in the manuscript.

The appropriateness of the statistical tests used in the study is another key focus of the review. The statistician should verify that the tests are suitable for the type and distribution of the data, ensuring that the manuscript justifies the choice of each statistical method. For studies involving multiple comparisons, it is crucial to assess whether corrections, such as Bonferroni or Holm adjustments, have been appropriately applied to control the false discovery rate. The handling of covariates should also be reviewed, with an emphasis on ensuring that any adjustments or interactions are well-justified and correctly modeled. The analysis should be robust, and the methods should be thoroughly explained to allow for replication and verification by other researchers.

The interpretation of statistical results should be clear, accurate, and aligned with the data presented. The statistician should ensure that the manuscript does not overstate or misinterpret the findings. This includes verifying that the results are presented in a manner that is easy to understand, with all relevant statistical measures, such as confidence intervals and effect sizes, reported alongside p-values. This approach provides a more comprehensive understanding of the results and their implications. It is also important for the manuscript to distinguish between statistical significance and clinical relevance, particularly in medical research, where the practical implications of findings may be as important as the statistical outcomes.

Transparency in reporting is essential for the credibility and reproducibility of research. The statistician should ensure that the statistical methodology is described in sufficient detail, including the specific software, packages, and versions used. This level of detail allows other researchers to replicate the study and verify the findings. Additionally, where applicable, the manuscript should include data-sharing statements to promote transparency and enable further research based on the same data. The discussion of limitations is also crucial; the statistician should ensure that any limitations related to the statistical methods and analyses are clearly articulated, including potential sources of bias and uncertainty.