As a domain expert in statistical analysis and research methodology, I often encounter questions about the interpretation of effect sizes in various studies. The concept of effect size is crucial in determining the practical significance of findings beyond their statistical significance. It quantifies the magnitude of a phenomenon and provides a standardized measure that can be used to compare the strength of effects across different studies. Effect sizes are particularly important in fields such as psychology, education, and medicine, where the impact of interventions or the differences between groups are often subtle and not easily discernible without a standardized metric. One of the most common measures of effect size is Cohen's d, which is calculated as the difference between two means divided by a standard deviation. **Cohen suggested that d=0.2 be considered a 'small' effect size, 0.5 represents a 'medium' effect size, and 0.8 a 'large' effect size.** This classification provides a general guideline for researchers to interpret the magnitude of their findings. However, it is important to note that what constitutes a "good" effect size can vary depending on the context and the field of study. In some disciplines, even a small effect size might be considered meaningful if the stakes are high, such as in medical treatments where even a slight improvement can lead to significant benefits for patients. On the other hand, in areas where the cost of implementing an intervention is high, a larger effect size might be necessary to justify the resources required. When evaluating the quality of an effect size, it is also important to consider the sample size and the variability within the data. A larger sample size can provide more precise estimates of the effect size, and a smaller variability can make even a small effect size more meaningful because it indicates that the effect is consistent across the sample. Moreover, the choice of what constitutes a good effect size should also be informed by theoretical considerations and practical implications. For instance, in a theoretical study aiming to understand a basic psychological process, a small effect size might be of great interest because it can shed light on subtle but important phenomena. In contrast, in applied settings, such as education or public health, a larger effect size might be more desirable because it is more likely to have a noticeable impact on a population level. It is also worth mentioning that the concept of effect size is not limited to Cohen's d. Other measures such as eta-squared (η²), partial eta-squared (η²p), and odds ratios are also used depending on the design of the study and the type of data being analyzed. Each of these measures has its own advantages and limitations, and the choice of which to use should be guided by the research question and the nature of the data. In conclusion, determining what is a good effect size is a complex decision that depends on multiple factors, including the field of study, the importance of the outcome, the sample size, the variability in the data, and the theoretical and practical implications of the findings. While Cohen's guidelines provide a starting point, researchers should use their professional judgment and consider the broader context when interpreting the magnitude and significance of an effect size. read more >>

Cohen suggested that d=0.2 be considered a 'small' effect size, 0.5 represents a 'medium' effect size and 0.8 a 'large' effect size. This means that if two groups' means don't differ by 0.2 standard deviations or more, the difference is trivial, even if it is statistically signficant.read more >>