Distinguishing Statistical Significance from Effect Size- Understanding the Key Differences
What is the difference between statistical significance and effect size? This is a question that often arises in the field of statistics and research methodology. While both concepts are essential in understanding the results of a study, they serve different purposes and provide distinct information. In this article, we will delve into the differences between these two key statistical measures.
Statistical significance refers to the likelihood that the observed results in a study are not due to chance. It is a measure of the evidence against the null hypothesis, which states that there is no effect or difference between groups. When a result is statistically significant, it means that the observed effect is unlikely to have occurred by random chance alone. This is typically determined through hypothesis testing, where a p-value is calculated to assess the strength of the evidence against the null hypothesis.
On the other hand, effect size measures the magnitude of the difference or relationship between groups or variables. It quantifies the strength of the observed effect, regardless of its statistical significance. Effect size provides information about the practical significance of the findings, indicating the size of the effect in real-world terms. Commonly used effect size measures include Cohen’s d for mean differences, Pearson’s r for correlations, and odds ratios for categorical outcomes.
One of the key differences between statistical significance and effect size is their focus. Statistical significance focuses on the probability of the observed results occurring by chance, while effect size focuses on the magnitude of the observed effect. It is important to note that a statistically significant result does not necessarily imply a large effect size. Conversely, a non-significant result does not necessarily mean that the effect is negligible or that the study was poorly designed.
Another difference lies in their interpretation. Statistical significance is often used to determine whether a result is publishable or worthy of further investigation. If a result is statistically significant, it suggests that the findings are reliable and not due to random chance. However, statistical significance alone does not provide information about the practical importance or the implications of the observed effect.
Effect size, on the other hand, is crucial for understanding the practical significance of the findings. A large effect size indicates a substantial difference or relationship between groups or variables, while a small effect size suggests a minimal or trivial difference. Effect size is also useful for comparing the magnitude of effects across different studies or research areas.
In conclusion, statistical significance and effect size are two distinct concepts in statistics. While statistical significance determines the likelihood of the observed results occurring by chance, effect size quantifies the magnitude of the observed effect. Both measures are important in understanding the results of a study, but they serve different purposes and provide different insights. Researchers should consider both statistical significance and effect size when interpreting their findings and drawing conclusions.
