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Related Concept Videos

Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
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.
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Comparing Experimental Results: Student's t-Test01:09

Comparing Experimental Results: Student's t-Test

The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...
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: 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:
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
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Updated: Jun 27, 2026

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

Standardized or simple effect size: what should be reported?

Thom Baguley1

  • 1Division of Psychology, Nottingham Trent University, Nottingham, UK. Thomas.Baguley@ntu.ac.uk

British Journal of Psychology (London, England : 1953)
|November 20, 2008
PubMed
Summary
This summary is machine-generated.

Psychologists should report effect size in research, but often use inappropriate statistics. This paper advocates for simple effect size over standardized measures and recommends confidence intervals for robust reporting.

Related Experiment Videos

Last Updated: Jun 27, 2026

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials
08:36

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

Area of Science:

  • Psychology
  • Quantitative Research Methods

Background:

  • Reporting effect size is best practice in psychology, but its application is inconsistent.
  • There is a lack of clarity regarding the appropriate use of effect size statistics in published research.

Purpose of the Study:

  • To examine the reporting of standardized effect size in psychological literature.
  • To explore factors that can distort standardized effect size estimates.
  • To provide guidelines for selecting and reporting appropriate effect size metrics.

Main Methods:

  • Analysis of the practice of reporting point estimates of standardized effect size.
  • Consideration of factors affecting standardized effect size (reliability, range restriction, design differences).
  • Development of guidelines for choosing and reporting effect size metrics.

Main Results:

  • Standardized effect size can be distorted by various factors unless corrected.
  • Simple (unstandardized) effect size is generally more robust and versatile than standardized effect size.
  • Confidence intervals are recommended for indicating plausible effect size ranges.

Conclusions:

  • Researchers should prioritize simple effect size over standardized effect size.
  • The choice of effect size metric requires careful consideration of research goals, context, and audience needs.
  • Confidence intervals are crucial for transparent and informative effect size reporting.