Effect Size
The short answer · 10 words
In short
Statistical measure of the size of a difference or connection.
Flow-alerta.cab (2026): Effect Size. https://flow-alerta.cab/en/glossar/effect-size. Editorially reviewed on: 10 October 2026.
How much detail would you like?
Practical
High (Essential for clean A/B tests, data science and scientific evaluation)
Hypothetical example: An A/B test gives p < 0.01 and an increase in the click-through rate by 0.05 percentage points. The team reviews confidence interval and economic benefits.
Basis for decision-making for website relaunches, price changes and the introduction of new marketing measures.
At a glance
- Core
- Effect measures such as mean value difference, Cohen's d, correlation or odds ratio describe the size and direction of an effect. The p-value evaluates data under a null hypothesis and is not a measure of effect size. Practical relevance and uncertainty must be assessed separately.
- Use
- High (Essential for clean A/B tests, data science and scientific evaluation)
- Example
- Hypothetical example: An A/B test gives p < 0.01 and an increase in the click-through rate by 0.05 percentage points. The team reviews confidence interval and economic benefits.
- Limits
- Confuse significance with effect size; mix percent and percentage points; ignore estimation uncertainty or business context.
In context
Questions about this term
Why is the p-value not enough?
It does not measure effect size or practical significance. Large samples can statistically reveal small actual differences.
Are 0.2/0,5/0.8 for Cohen's d fixed limits?
No, these are common orientation conventions that must fit the technical context.
All details and background
Effect measures such as mean value difference, Cohen's d, correlation or odds ratio describe the size and direction of an effect. The p-value evaluates data under a null hypothesis and is not a measure of effect size. Practical relevance and uncertainty must be assessed separately.
ELI5 – Explained simply
A change can be statistically noticeable and still small. The effect strength describes how large it is.
Why it matters in practice
High (Essential for clean A/B tests, data science and scientific evaluation)
Example
Hypothetical example: An A/B test gives p < 0.01 and an increase in the click-through rate by 0.05 percentage points. The team reviews confidence interval and economic benefits.
Pitfalls and limits
- Confuse significance with effect size; mix percent and percentage points; ignore estimation uncertainty or business context.
Origin & history
Effect sizes are part of the statistical evaluation and meta-analysis; standardized measures were disseminated by Cohen, among others.
Use in mid-sized companies
- Basis for decision-making for website relaunches, price changes and the introduction of new marketing measures.
Related terms
- A/B testing
- p-value
- Statistical significance
- Hypothesis test
- Cohen's d
Matching Flow-alerta service
AI opportunity analysis & roadmapAI consulting: Examine the suitability, data and prerequisites of a possible use case on the subject of “effect strength”.Matching tool / use case
View use casesUse Case: Evaluation of conversion tests and experiments / Tools: scipy.stats, pingouin, RSources
Quellenprüfung: 10.10.2026. Definition und dokumentierte Einordnung abgeglichen; Beispiele beschreiben mögliche Anwendungen.
Editorially reviewed on: