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Introduction

In statistical analysis, Cohen's d and effect size correlation (r) are commonly used measures to quantify the size of an observed effect.

Cohen's d is a standardized measure of effect size that quantifies the difference between two group means in terms of standard deviations. It is widely used in statistics to communicate the magnitude of the difference between groups in a study.

Interpretation of Cohen's d involves assessing the size of the effect, and common benchmarks are:

Small Effect: d≈0.2
Medium Effect: 0.2< d ≈0.5
Large Effect: d>0.5

The sign of d indicates the direction of the effect: positive d suggests that the mean difference is in the predicted direction, while negative d suggests the opposite.

Effect size correlation (r) represents the correlation between two variables and is often used as a measure of the strength and direction of a relationship.

Interpretation of effect size correlation involves assessing the strength of the correlation, and common benchmarks are:

Small Effect: r≈0.1
Medium Effect: 0.1< r ≈0.3
Large Effect: r>0.3

The sign of r indicates the direction of the relationship: positive r suggests a positive correlation, while negative r suggests a negative correlation.

This article presents a tool designed to calculate Cohen's d and effect size correlation (r) using the T-value and degrees of freedom. Let's explore the variables involved and the calculation process.

Variables Definitions

T-value (t-value): The T-value is a statistical measure that indicates the number of standard deviations a data point is from the mean. It is often obtained from hypothesis tests comparing sample means.
Degrees of Freedom (df): Degrees of freedom represent the number of values in the final calculation of a statistic that are free to vary. It is a critical parameter, especially in T-tests, and is essential for estimating population parameters.
Cohen's d: Cohen's d is a standardized measure of effect size that quantifies the difference between two group means in terms of standard deviations.
Effect Size Correlation (r): Effect size correlation (r) represents the correlation between two variables. It is commonly used as a measure of the strength and direction of a relationship between variables.