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Related Concept Videos

Statistical Significance01:37

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...
One-Way ANOVA: Equal Sample Sizes01:15

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...
Identifying Statistically Significant Differences: The F-Test01:14

Identifying Statistically Significant Differences: The F-Test

The F-test is used to compare two sample variances to each other or compare the sample variance to the population variance. It is used to decide whether an indeterminate error can explain the difference in their values. The underlying assumptions that allow the use of the F-test include the data set or sets are normally distributed, and the data sets are independent of each other. The test statistic F is calculated by dividing one variance by another. In other words, the square of one standard...
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
Sample Size Calculation01:19

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...
Significance Testing: Overview01:04

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...

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Related Experiment Video

Updated: May 22, 2026

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

Published on: September 11, 2021

On effect size.

Ken Kelley1, Kristopher J Preacher

  • 1Department of Management, Mendoza College of Business, University of Notre Dame, Notre Dame, IN 46556, USA. kkelley@nd.edu

Psychological Methods
|May 2, 2012
PubMed
Summary
This summary is machine-generated.

Researchers need clear definitions for effect sizes, which quantify phenomenon magnitude. This study proposes an inclusive definition, linking effect size to research questions and improving statistical reporting practices.

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Area of Science:

  • Statistics
  • Research Methodology

Background:

  • Growing emphasis on reporting effect sizes and confidence intervals in research.
  • Inconsistent definitions and usage of 'effect size' in scientific literature.

Purpose of the Study:

  • To propose a clear, inclusive definition of effect size.
  • To discuss facets, corollaries, and ideal qualities of effect sizes.
  • To encourage consistent reporting and interpretation of effect sizes in research.

Main Methods:

  • Defining effect size as a quantitative reflection of phenomenon magnitude linked to a research question.
  • Discussing three facets: dimension, measure/index, and value.
  • Outlining ten corollaries derived from the proposed definition.
  • Reviewing ideal qualities of effect sizes and developments in the literature.

Main Results:

  • A general definition of effect size is proposed, encompassing existing definitions.
  • The definition emphasizes the link between effect size and the specific research question.
  • Ten corollaries and ideal qualities for effect sizes are presented.

Conclusions:

  • The proposed definition aims to resolve definitional ambiguity and promote consistent use of effect sizes.
  • Accompanying effect sizes with interval estimates is crucial for acknowledging uncertainty.
  • This work seeks to improve the practice of reporting and interpreting effect sizes in scientific research.