Effect size

Effect Size

Effect size (/ɪˈfɛkt saɪz/) is a statistical concept that measures the strength of the relationship between two variables in a statistical population, or the size of an experimental effect within a sample.

Etymology

The term "effect size" originated in the field of statistics and has been used since the early 20th century. It is derived from the words "effect", meaning a change that is a result or consequence of an action or other cause, and "size", referring to the magnitude or extent of something.

Definition

In statistics, the effect size is a quantitative measure of the magnitude of the experimental effect. The larger the effect size, the stronger the relationship between two variables. It is a crucial tool in meta-analysis studies because it allows researchers to go beyond the simple question of whether an effect exists, to instead ask about the size of the effect.

Types of Effect Size

There are several types of effect size, including:

• Cohen's d: This is used to indicate the standardized difference between two means.
• Eta squared: This is used in ANOVA to estimate the proportion of variance explained by a factor in a population.
• Odds ratio: This is used in binary data to express the relative odds of an event happening under two different conditions.

Calculation

The calculation of effect size depends on the type of effect size being calculated. For example, Cohen's d is calculated by subtracting the mean of one group from the mean of another, and then dividing the result by the pooled standard deviation.

Importance

Effect size is important in research because it provides a measure of the strength of a relationship that is independent of sample size. This makes it a more reliable statistic for comparing the results of different studies.

Related Terms

• Statistical significance
• Power (statistics)
• Meta-analysis
• Cohen's d
• Eta squared
• Odds ratio

External links

• Medical encyclopedia article on Effect size
• Wikipedia's article - Effect size

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