Department of Family and Community Medicine
King Abdulaziz University, KSA
NSBSTAT Knowledge Centre
Practical explanations for designing studies, choosing statistical methods, interpreting results, and reporting research clearly—written for researchers, clinicians, and postgraduate students.
Featured guide
01
A practical foundation for reliable clinical evidence
Clinical & Study Design
A practical roadmap for using biostatistics in clinical research design, data analysis, and reporting.
Topic collection
A practical roadmap for using biostatistics in clinical research design, data analysis, and reporting.
Define the primary endpoint and estimand before choosing a statistical test.
Read guideHow to calculate sample size correctly and prevent common power-analysis mistakes in research protocols.
Power the study for its primary outcome and primary comparison.
Read guideA clear explanation of Kaplan-Meier curves, censoring, and survival comparison for medical researchers.
Define the time origin, event, and censoring rules before creating the curve.
Read guideHow to design cross-sectional studies that produce credible and actionable findings.
Define the target population, sampling frame, eligibility criteria, and observation period precisely.
Read guideTopic collection
Decision framework for choosing between ANOVA and regression in applied statistical projects.
ANOVA and ordinary linear regression are expressions of the same general linear-model framework.
Read guideA practical interpretation guide that goes beyond p-value thresholds for better scientific decisions.
A p-value is calculated under a statistical model; it is not the probability that the null hypothesis is true.
Read guideA checklist-based guide to avoid common statistical mistakes before submission or peer review.
Many statistical failures begin with an unclear question, endpoint, or unit of analysis—not with software.
Read guideTopic collection
Practical validation steps for building reliable questionnaires in health, education, and social research.
Validation is an accumulation of evidence for a particular use, population, language, and setting.
Read guideA reproducible data-cleaning workflow to improve validity and reduce analysis errors in health datasets.
Preserve raw data as immutable and create analysis data through versioned, repeatable code.
Read guideTopic collection
A people-first method for making statistical explanations clear, useful, source-backed, and machine-readable.
Write for the researcher's decision first; extraction-friendly structure should improve, not replace, substantive content.
Read guideFrom guidance to analysis
Use DataStatPro for guided statistical workflows, or work with NSBSTAT for study design, analysis, and publication support.