How many do I need? Basic principles of sample size estimation
Article first published online: 6 JUL 2004
Journal of Advanced Nursing
Volume 47, Issue 3, pages 297–302, August 2004
How to Cite
Devane, D., Begley, C. M. and Clarke, M. (2004), How many do I need? Basic principles of sample size estimation. Journal of Advanced Nursing, 47: 297–302. doi: 10.1111/j.1365-2648.2004.03093.x
- Issue published online: 6 JUL 2004
- Article first published online: 6 JUL 2004
- Submitted for publication 4 December 2003 Accepted for publication 9 February 2004
- sample size;
- midwifery/nursing research
Background. In conducting randomized trials, formal estimations of sample size are required to ensure that the probability of missing an important difference is small, to reduce unnecessary cost and to reduce wastage. Nevertheless, this aspect of research design often causes confusion for the novice researcher.
Aim. This paper attempts to demystify the process of sample size estimation by explaining some of the basic concepts and issues to consider in determining appropriate sample sizes.
Method. Using a hypothetical two group, randomized trial as an example, we examine each of the basic issues that require consideration in estimating appropriate sample sizes. Issues discussed include: the ethics of randomized trials, the randomized trial, the null hypothesis, effect size, probability, significance level and type I error, and power and type II error. The paper concludes with examples of sample size estimations with varying effect size, power and alpha levels.
Conclusion. Health care researchers should carefully consider each of the aspects inherent in sample size estimations. Such consideration is essential if care is to be based on sound evidence, which has been collected with due consideration of resource use, clinically important differences and the need to avoid, as far as possible, types I and II errors. If the techniques they employ are not appropriate, researchers run the risk of misinterpreting findings due to inappropriate, unrepresentative and biased samples.