Journal of Statistics and Data Analytics

Peer Reviewers

Peer Review Board: The peer reviewers of Journal of Statistics and Data Analytics (JSDA) consist of academics, researchers, statisticians, data analysts, and quantitative-methods specialists whose expertise supports the scientific evaluation of manuscripts submitted to the journal. Reviewers contribute to maintaining scholarly quality, methodological rigor, statistical validity, academic integrity, and scientific relevance.

The journal assigns reviewers according to the scientific and methodological characteristics of each submitted manuscript. Relevant areas of expertise include:

• Evaluate manuscripts objectively, independently, fairly, and constructively.
• Assess originality, scientific significance, methodological rigor, and relevance to the journal's scope.
• Evaluate the appropriateness of statistical methods, models, assumptions, and analytical procedures.
• Assess whether conclusions are adequately supported by the data and analyses presented.
• Evaluate model validation, diagnostic procedures, uncertainty, and interpretation where applicable.
• Assess computational methods, simulation procedures, algorithms, and reproducibility where relevant.
• Evaluate tables, figures, data visualizations, and reporting of statistical results.
• Provide clear, evidence-based, professional, and constructive recommendations.
• Identify significant methodological, statistical, ethical, citation, or originality concerns.
• Maintain confidentiality throughout the review process.
• Declare potential conflicts of interest before accepting a review assignment.
• Support the journal's standards of publication ethics and academic integrity.

Journal of Statistics and Data Analytics applies a double-blind peer-review process. The identities of authors and reviewers are concealed from each other to support an objective, independent, and fair evaluation. Manuscripts that pass the initial editorial screening are normally evaluated by at least two independent reviewers whose expertise is relevant to the statistical, computational, methodological, or applied aspects of the manuscript.

Reviewers are selected based on their academic qualifications, research experience, publication record, methodological competence, subject expertise, and independence.

Where reasonably possible, the Editorial Team assigns reviewers whose expertise closely matches the statistical methods, data characteristics, computational approaches, and application domain of the submitted manuscript. Potential conflicts of interest are considered during reviewer selection.

Manuscripts submitted for peer review must be treated as confidential documents. Reviewers must not copy, distribute, discuss, share, or disclose manuscript content to unauthorized parties. Unpublished data, statistical methods, algorithms, analytical procedures, results, figures, tables, or other information obtained through peer review must not be used for personal, academic, professional, or commercial advantage.

Reviewers should promptly notify the responsible editor if they identify or reasonably suspect plagiarism, duplicate publication, data fabrication, data falsification, statistical manipulation, inappropriate image manipulation, citation manipulation, undisclosed conflicts of interest, or other forms of research or publication misconduct. Concerns should be communicated confidentially and supported by relevant information where possible.

When relevant to the manuscript, reviewers are encouraged to consider:

• Whether the selected statistical method is appropriate for the research question and data structure;
• Whether important statistical assumptions are stated and evaluated;
• Whether sample size, sampling procedures, and experimental design are sufficiently justified;
• Whether missing data, outliers, multiple testing, confounding, or model uncertainty have been appropriately addressed;
• Whether effect estimates, uncertainty measures, confidence intervals, or other relevant statistical quantities are adequately reported;
• Whether model diagnostics and validation procedures are sufficient;
• Whether results are interpreted without overstating statistical evidence;
• Whether analytical procedures are described with sufficient detail to support reproducibility.

Acknowledgment to Reviewers

The Editorial Team gratefully acknowledges the contribution of peer reviewers who dedicate their expertise, time, and scholarly judgment to maintaining the academic quality and integrity of Journal of Statistics and Data Analytics. Their contribution is essential to supporting rigorous, fair, constructive, and methodologically sound scholarly publishing.

Institutional affiliations, academic-profile identifiers, and areas of expertise displayed on this page may be updated periodically to maintain the accuracy of the journal's Peer Review Board information.

Journal of Statistics and Data Analytics
Statistics • Data • Analytics

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