Sample size is the number of individuals or observations included in a sample for a particular study. It is a critical aspect of research design and is crucial in determining the precision, reliability, and generalizability of study findings.
The sample size is typically determined based on the research objectives, the nature of the study, statistical considerations, and practical constraints.
Factors influencing sample size determination include:
- Effect size: It refers to the magnitude of the effect or difference the study aims to detect. Larger effects may require smaller sample sizes to achieve statistical significance.
- Significance Level (α): It is the probability of rejecting a true null hypothesis. While commonly set at 0.05, researchers may choose different levels of significance.
- Power (1-β): This is the probability of rejecting a false null hypothesis correctly. Researchers typically aim for a high level of power, often 0.80 or 0.90.
- Population variability:** The amount of variability or dispersion in the population. Higher variability may require larger sample sizes.
- Research design: The design of the study, including the type of analysis planned (e.g., t-tests, ANOVA, regression), can influence the required sample size.
- Type of study: The nature of the study, such as observational, experimental, or survey research, can impact the determination of an appropriate sample size.
List of recommended resources #
For a broad overview #
Sample size calculations: basic principles and common pitfalls
This paper discusses the basic principles of sample size calculations, the most common challenges and the reporting of these calculations.
Sample size: how many participants do I need in my research?
This paper aims to highlight the centrality of sample size estimations in health research. It illustrates with the help of examples for a better understanding of the basic concepts involved in the calculation of sample size.
For in depth understanding #
Determining Sample Size and Power in Research: A Manual for Researchers
Written by J. P. Verma and Priyam Verma, this book discusses the process of computing sample size for a desired power, by fixing error rate(a) and effect size in different statistical tests.
Small Sample Size Solutions: A Guide for Applied Researchers and Practitioners
Edited by Rens van de Schoot and Milica Miočević, this book provides guidelines and tools for implementing solutions to issues that arise in small sample research. It will enable social and behavioral science researchers to test their hypotheses even when the statistical model required for answering their research question is too complex for the sample sizes they can collect.
Case study #
This study evaluates ground and satellite-based approaches to estimating crop yields and yield responsiveness to inputs, using data on maize from Eastern Uganda. It is observed that satellite-based yield measures lead to more significant results due to larger sample sizes.
Pioneering the Green Sukuk: Three Years On
This note published by the World Bank is a stocktaking of the first three years of the green sukuk. Due to the small sample size and limited data, the analysis is limited in scope. However, it covers the basics of what constitutes a green sukuk, a brief history as well as an assessment of the green sukuk market to date.
References #
Determining sample size: how to make sure you get the correct sample size
Sample Size in Statistics (How to Find it): Excel, Cochran’s Formula, General Tips
