Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Statistical Significance

Statistical Significance

Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...

Sample Size Calculation

Sample Size Calculation

Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...

Statistical Methods for Analyzing Epidemiological Data

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:

One-Way ANOVA: Equal Sample Sizes

One-Way ANOVA: Equal Sample Sizes

One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...

Fisher's Exact Test

Fisher's Exact Test

Fisher's exact test is a statistical significance test widely used to analyze 2x2 contingency tables, particularly in situations where sample sizes are small. Unlike the chi-squared test, which approximates P-values and assumes minimum expected frequencies of at least five in each cell, Fisher's exact test calculates the exact probability (P-value) of observing the data or more extreme results under the null hypothesis. This feature makes it especially valuable when the assumptions of the...

Significance Testing: Overview

Significance Testing: Overview

Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Empirically Based Mean Effect Size Distributions for Universal Prevention Programs Targeting School-Aged Youth: A Review of Meta-Analyses.

Prevention science : the official journal of the Society for Prevention Research·2018

Same author

Promoting Positive Youth Development Through School-Based Social and Emotional Learning Interventions: A Meta-Analysis of Follow-Up Effects.

Child development·2017

Same author

A meta-analysis of indicated mental health prevention programs for at-risk higher education students.

Journal of counseling psychology·2017

Same author

Social-Emotional Competence: An Essential Factor for Promoting Positive Adjustment and Reducing Risk in School Children.

Child development·2017

Same author

A Meta-Analysis of the Impact of Universal and Indicated Preventive Technology-Delivered Interventions for Higher Education Students.

Prevention science : the official journal of the Society for Prevention Research·2016

Same author

Studying Program Implementation Is Not Easy but It Is Essential.

Prevention science : the official journal of the Society for Prevention Research·2015

Search research articles

Related Experiment Video

Updated: Jun 25, 2026

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

How to select, calculate, and interpret effect sizes.

Joseph A Durlak¹

¹Department of Psychology, Loyola University Chicago, 6525 N. Sheridan Road, Chicago, IL 60626, USA. jdurlak@luc.edu

Journal of Pediatric Psychology

|February 19, 2009

Summary

This guide explains effect sizes (ESs), crucial for understanding research impact. It details how to choose, calculate, and interpret ESs in various study types for better research comprehension.

Area of Science:

Quantitative Psychology
Research Methodology
Statistical Analysis

Background:

Effect sizes (ESs) are vital for quantifying the magnitude of research findings.
Understanding ESs is crucial for interpreting the practical significance of study results.
Previous research has highlighted the need for clear guidelines on ES usage.

Purpose of the Study:

To provide comprehensive guidelines on selecting, calculating, and interpreting effect sizes (ESs).
To emphasize the critical role and contribution of ESs in scientific research.
To aid researchers and readers in understanding the meaning and application of ESs.

Main Methods:

Definition and conceptualization of effect sizes (ESs).
Discussion of commonly used ESs in group and correlational research designs.

More Related Videos

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

Related Experiment Videos

Last Updated: Jun 25, 2026

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

Provision of resources for differentiating ES types and calculation modifications.

Main Results:

Effect sizes (ESs) are clearly defined, highlighting their importance in research.
Various types of ESs applicable to different study designs are explored.
Guidance is offered on selecting, calculating, and interpreting ESs based on study specifics.

Conclusions:

Clear guidelines for effect size (ES) selection, calculation, and interpretation are presented.
The article enhances the understanding of ESs for both research producers and consumers.
Proper use of ESs improves the clarity and impact of research reporting.