effect size

USAGE SUMMARY

"effect size" is a correct and usable phrase in written English, and it is often used in academic writing.
For example, "We found that the effect size of the intervention was statistically significant."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

59 human-written examples

Effect size (how effective is the intervention?).

Crime Science

Effect size.

Research and Practice in Technology Enhanced Learning

Standardized effect size.

Journal of Patient-Reported Outcomes

least discriminant analysis effect size.

AMB Express

"Conversion of effect size to percentile gain".

International Journal of Child Care and Education Policy

That estimated effect size was 1.62.

Cognitive Research: Principles and Implications

linear discriminant analysis effect size pipeline.

AMB Express

variance, d Cohen's d effect size indicator.

Latin American Economic Review

Thus, the effect size is possibly underestimated.

International Journal of Emergency Medicine

Effect size was calculated when possible.

The Journal of Headache and Pain

The effect size for difference was large.

Sports Medicine - Open

Expert writing Tips

Best practice

When reporting the "effect size", always include the specific measure used (e.g., Cohen's d, Pearson's r) and its confidence interval to provide a complete picture of the effect's magnitude and precision.

Common error

Don't assume that a statistically significant "effect size" is automatically practically important. A small effect can be statistically significant with a large sample size, but have little real-world relevance.

Linguistic Context

The phrase "effect size" functions as a noun, specifically referring to a quantitative measure. It is used to indicate the magnitude of an effect or relationship, often in the context of research or experimentation. Ludwig shows that it appears with statistical terms.

Expression frequency: Very common

Frequent in

Science

90%

Formal & Business

5%

News & Media

3%

Less common in

Academia

1%

Encyclopedias

0.5%

Wiki

0.5%

Ludwig's WRAP-UP

The phrase "effect size" is a commonly used term, especially in scientific and research contexts, to quantify the magnitude of an effect or relationship. As Ludwig AI confirms, the term is grammatically correct and primarily found in scientific literature. It functions as a noun and serves the purpose of providing an objective measure of the strength of an observed effect. When using "effect size", it's crucial to specify the measure employed (e.g., Cohen's d) and be mindful of the difference between statistical and practical significance. Related phrases include "magnitude of effect" and "strength of association", offering alternative ways to express the same concept.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

magnitude of effect

Emphasizes the extent or degree of impact.

strength of association

Focuses on the degree to which two variables are related.

treatment effect

Specifically refers to the impact of a treatment or intervention.

practical significance

Highlights the real-world importance or relevance of a finding.

clinical significance

Addresses the relevance of a finding to clinical practice.

statistical significance

Focuses on whether the result is likely due to chance alone.

Cohen's d

Specific statistical measure of effect size.

observed difference

Highlights the difference between two groups or conditions, serving as a general reference to "effect size".

degree of impact

Highlights the extent to which something is affected.

size of the impact

Highlights the magnitude of impact.

FAQs

How is "effect size" used in statistical analysis?

In statistical analysis, "effect size" measures the strength of the relationship between two variables on a numeric scale. It helps to determine the practical importance of a finding beyond statistical significance.

What are common measures of "effect size"?

Common measures of "effect size" include Cohen's d (for differences between means), Pearson's r (for correlations), and odds ratios (for categorical data). The choice depends on the type of data and research question.

How do I interpret a reported "effect size"?

Interpretation depends on the specific measure. For Cohen's d, 0.2 is considered small, 0.5 medium, and 0.8 large. Context is crucial; a small effect can still be meaningful in certain situations. Consider using "clinical significance".

What is the difference between "effect size" and statistical significance?

"Effect size" quantifies the magnitude of an effect, while statistical significance indicates whether the effect is likely due to chance. A study can have a large effect that isn't statistically significant, or vice versa. Use "practical significance" when appropriate.