Effect size

An effect size quantifies the magnitude of a difference or relationship. It may use original units, such as seconds, or a standardized scale. Choosing the metric and reporting uncertainty makes results more useful for interpretation, comparison, and future study planning than a significance label alone.

How large is the estimated difference or relationship on a scale that answers the research question?

How it works

Identify the quantity of interest and choose a metric before inspecting favorable results. A mean difference may be most understandable in original units; a standardized mean difference requires an explicit standardizer.
Calculate the estimate using a method appropriate to the design. Within-person and independent-group standardized effects need careful specification because their denominators and interpretations can differ.
Report the estimate, its interval, and enough information to interpret the scale. Discuss meaningful magnitudes in context and explain the metric when comparing studies or using the estimate for planning.

Evidence: (Calculating and Reporting Effect Sizes to Facilitate Cumulative Science: A Practical Primer for t-Tests and Anovas, n.d.); (American Statistical Association Releases Statement on Statistical Significance and P-Values, n.d.)

A proposed attention-task comparison

Teaching scenario

Synthetic example: An attention experiment compares response times under quiet and distracting conditions. The classroom team proposes reporting the average difference in milliseconds alongside a standardized effect, explaining exactly which variability measure enters its denominator. Learners then ask whether the same numerical difference would matter equally in a classroom exercise and an emergency warning task. The example contains no observed difference and no claim of practical benefit. It illustrates why an effect-size label needs a clearly defined outcome, design, and use context before anyone can judge its importance.

This example is constructed for learning. It does not report an observed research result.

What can you conclude?

Communicates estimated magnitude and supports cumulative comparisons when compatible metrics and designs are used.

Limits and common mistakes

Magnitude estimates remain uncertain and can be biased by study design or selective reporting.
Standardized effects depend on the variability used; conventional small or large labels do not establish practical importance.

A mistake to catch

Equating a small p-value with a large or useful effect, or comparing differently defined standardized effects as interchangeable.

Sources and inspection notes

Links checked October 21, 2026. Inspection scope is stated for each source; an access date is not a publication date.

American statistical association releases statement on statistical significance and p-values. (n.d.). https://www.amstat.org/asa/files/pdfs/p-valuestatement.pdf
Page 2, six principles Official ASA release reproducing the six principles. Full publisher article returned 403, so this source is the verified official access route. Opened and checked 21 October 2026.
Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t-tests and anovas. (n.d.). https://doi.org/10.3389/fpsyg.2013.00863Consulted source
Introduction and sections on standardized mean differences and within-subject designs Lakens, 2013, original methods paper. Supports metric/standardizer choices, design dependence, and cumulative use; not a universal practical-importance threshold. Opened and checked 21 October 2026.

Keep reading

Spielman, R. M., Jenkins, W. J., & Lovett, M. D. (2020). Psychology 2e. OpenStax. https://openstax.org/books/psychology-2e/pages/2-3-analyzing-findings

Nearest related OpenStax Psychology 2e reading. This is a course connection, not evidence for every specialist detail above.

Specialist reading: Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t-tests and anovas. (n.d.). https://doi.org/10.3389/fpsyg.2013.00863Consulted source