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The statistical review has remained one of the best and most trustworthy ways to detect flaws in scientific communication, filter out low-quality research, and introduce significant contributions to the field’s improvement.
Accurate and rigorous data and statistical analysis in academic/scientific manuscripts are important to reproduce the findings presented. It also enhances the research’s authenticity and results. However, mistakes may occur at any stage of the statistical analysis process due to human error. Therefore, statistical analysis is an essential step in the peer review process to detect errors and enhance the quality of academic papers.
Peer Review – A Method to Rely On
Statistical review is also known as peer review, in which the researchers statistically review the findings and information published by other researchers. However, the degree to which the peer review is good and authentic has a lot to do with its reliability. It is, therefore, a great responsibility on the reviewers and should be attended to with great care.
A good peer reviewer should;
- Assess the originality and the importance of the study
- Correctly evaluate the process of the collection and the analysis of data
- Clearly present all the gathered information in an understandable and comprehensible manner
How to become a good Peer reviewer?
Many young investigators do not understand the intricacies of the duty and fail to present the accuracy and seriousness in their review. It occurs with the lack of formal training and the sense to consult a statistical expert while performing the duty. Therefore, the peer reviewer must follow these guidelines;
- Before agreeing to the review
- It is important that you are highly familiar with the manuscript you are responsible for reviewing.
- Ensure that you have ample time to invest in the manuscript and develop an authentic review within the deadline.
- You should also not have a conflict of interest that might influence your review results.
- When writing your report
- Ensure to balance friendliness and constructivism with criticism and attentiveness.
- The results of your review should technically be sound, and the claims must be supported with the presented data.
- Professionally assess the strengths and importance of the work combined with clear recommendations on its improvements.
- Detailed Analysis
The peer reviewers must conduct a deep analysis of the manuscript provided. For example, they have a keen eye on;
- Whether the title and abstract present the work properly
- If the results and discussion are presented in a logical, understandable, and comprehensible way
- If there are any ethical issues in position, they must also be reposted carefully and clearly.
- While Organizing your review
- Ensure that you start with the paper’s summary describing what the researcher/author did in the manuscript.
- Then, make general comments about the work that might include your views on the findings and how data is presented.
- Lastly, enlist some important points helping the researcher to improve the work and how these points can be implemented.
- When uploading the report
- Indicate whether the manuscript is reliable and credible enough to be accepted without changes or after revision, or it should be rejected wholly.
Questions to consider in a Statistical Review
Besides following the above guidelines, the evaluators can consider the following questions while conducting a statistical review:
- Has the researcher followed the relevant reporting guidelines?
- Has the researcher stated the deficiencies of the previous research in the Introduction?
- Are the research’s methods discussed in detail?
- Is it possible to reproduce the study results?
- Does the study represent any missing data or variables?
- Is enough information presented to verify the results?
- Hill J. A. (2016). How to Review a Manuscript. Journal of electrocardiology, 49(2), 109–111. https://doi.org/10.1016/j.jelectrocard.2016.01.001
- Hernandez, L. V. (2009). Becoming a reviewer is good for you—the peer-review process. Gastrointestinal Endoscopy, 70(6), 1159–1160. https://doi.org/10.1016/j.gie.2009.10.026
- Ravichandran, J. (2010). A review of preliminary test-based statistical methods for the benefit of Six Sigma quality practitioners. Statistical Papers, 53(3), 531–547. https://doi.org/10.1007/s00362-010-0359-9