Overview
Effect size is a crucial concept in statistics that helps researchers understand the magnitude of effects in their studies. Unlike p-values, which only indicate whether an effect exists, effect size provides a measure of how strong that effect is. This is particularly important in fields like psycho...
Key Terms
Example: A Cohen's d of 0.5 suggests a medium effect size.
Example: A Pearson's r of 0.8 indicates a strong positive correlation.
Example: Higher power increases the likelihood of detecting an effect if it exists.
Example: A 95% confidence interval suggests that if the study were repeated, 95% of the intervals would contain the true effect size.
Example: Meta-analysis can provide a more comprehensive understanding of effect sizes across studies.
Example: The null hypothesis might state that a new drug has no effect compared to a placebo.