Hanyang Med Rev.  2015 Feb;35(1):40-43. 10.7599/hmr.2015.35.1.40.

Understanding Effect Sizes

• Nahm FS

• KMID: 2149103
• DOI: http://doi.org/10.7599/hmr.2015.35.1.40

Abstract

In most medical research the P value is commonly used to describe test results. Because the power of statistical test is influenced by sample size, the null hypothesis can be rejected (P<0.05) in most cases if the sample size is tremendously big even if the real difference (or relationship) is extremly small. To overcome the weakness of using the P value, effect size can be used in the statistical analysis. Effect size can be defined as the "degree to which the phenomenon (difference or relationship) is present in the population". The effect size is used in sample size calculation, data interpretation and conducting meta-analysis. This manuscript describes limitations in using the P value and further introduces the concept of effect size.

Keyword

Data Interpretation, Statistical; Meta-Analysis; Research Design

MeSH Terms

Data Interpretation, Statistical
Research Design
Sample Size

Figure

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  Fig. 1 Example of effect size=1.96. The mean of the treatment group is in the range of upper 5% of the control group.

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