How does sample size relate to statistical power?
Sample size—the number of subjects in a study—plays an important role in determining the power of a study. Because researchers can use only a sample of the population when studying a phenomenon, the number of observations made in the sample must be large enough so the outcomes generated by the sample approximate the outcomes that would be generated if the entire population were studied. The larger the sample size, the higher the probability of rejecting a false null hypothesis. Thus, increasing the sample size increases the power of a study. What is effect size? Effect size represents the magnitude of the differences between two groups (for instance, the intervention group vs. the control group). In experimental studies, effect size is important because it tells us about the size of the effect an intervention has on the phenomenon being studied. A statistically significant outcome simply tells us whether or not a difference between groups exists. It doesn’t tell us the magnitude of the
Related Questions
- What is expected in terms of power calculations and sample size estimates for the formative studies, early-phase trials and pilot studies to be included in this RFA?
- What is the required sample size of a data set. Using statistical tests (CHI squared)?
- How do I calculate power or sample size for meta-analyses?
