Sample size determination: Principles and applications for health research
DOI:
https://doi.org/10.71357/hsij.v3i1.63Keywords:
Sample size determination, Statistical power, Confidence levels, WHO guidelines, Research methodologyAbstract
Background: Determining an appropriate sample size is a crucial aspect of research design, ensuring validity, reliability, and generalizability of findings. An inadequate sample size increases the risk of Type II errors, while an excessively large sample may lead to resource inefficiencies and a higher likelihood of Type I errors. Understanding the principles of sample size determination, including statistical power, confidence levels, and margin of error, is essential for producing accurate and meaningful research outcomes.
Objective: This review explores the principles of sample size determination, calculation methods for various research designs, and practical applications. It also discusses challenges in determining the optimal sample size and examines international guidelines, such as those issued by the World Health Organization (WHO), to enhance the accuracy and credibility of research findings.
Discussion: Sample size determination varies depending on research design, including surveys, experiments, and clinical trials. This review highlights key statistical considerations such as confidence intervals, statistical power, and the role of design effects. Additionally, practical challenges such as resource constraints, parameter misestimation, and population diversity are discussed. Technological advancements, including statistical software, are also examined for their role in improving sample size calculations and research efficiency.
Conclusion: Adhering to established principles and leveraging modern tools for sample size determination enables researchers to optimize study designs and enhance the validity of findings. Implementing international guidelines minimizes bias and ensures the robustness of results. Ultimately, accurate sample size estimation contributes to high-quality scientific studies that support evidence-based decision-making and progress across various disciplines.
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