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Everything about Effect size

Statistical measure of the magnitude of a phenomenon

In statistics, an effect size is a quantitative measure of the magnitude and often direction of a phenomenon. It can refer to the value of a statistic calculated from a sample of data, the value of one parameter for a hypothetical population, or the equation that operationalizes how statistics or parameters lead to the effect size value. Examples of effect sizes include the correlation between two variables, the regression coefficient in a regression, the mean difference, the standardised mean differences, and the risk of a particular event. Effect sizes are a complementary tool for statistical hypothesis testing, and play an important role in statistical power analyses to assess the sample size required for new experiments. Effect size calculations are fundamental to meta-analysis, which aims to provide the combined effect size based on data from multiple studies. The group of data-analysis methods concerning effect sizes is referred to as estimation statistics. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Effect sizeA 5-minute orientation
  2. Skim a recent paper: Effect size guidelines for cross-lagged effects.Influential work since 2022
  3. Pick up a classic book: Advances in Ecological ResearchThe most-reprinted book on the subject
  4. Read the landmark paper: Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAsCited 8,963 times
  5. Go sideways: Pooled varianceA closely related topic
  6. Go sideways: Student's t-testA closely related topic

📖 Definitions

Noun A measure of the strength or magnitude of the effect of an independent variable on a dependent variable in an experiment or a quasi-experiment.

🏆 Landmark papers (most cited)

🆕 Influential since 2022

📚 Books worth reading

Ranked by number of editions on Open Library, a rough measure of how lasting a book is.

🔗 Related topics