Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Bias01:22

Bias

7.2K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
7.2K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

1.3K
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
1.3K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

4.0K
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...
4.0K
Stratified Sampling Method01:16

Stratified Sampling Method

14.5K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
14.5K
Bonferroni Test01:10

Bonferroni Test

3.3K
The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
3.3K
Blind Procedures02:07

Blind Procedures

12.9K
Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...
12.9K

You might also read

Related Articles

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

Sort by
Same author

Glaucoma outcome studies using existing databases: opportunities and limitations.

Journal of glaucoma·2009
Same author

Evaluation of skin and respiratory doses and urinary excretion of alkylphosphates in workers exposed to dimethoate during treatment of olive trees.

Archives of environmental contamination and toxicology·2005
Same author

Small-sample bias and corrections for conditional maximum-likelihood odds-ratio estimators.

Biostatistics (Oxford, England)·2003
Same author

On the bias produced by quality scores in meta-analysis, and a hierarchical view of proposed solutions.

Biostatistics (Oxford, England)·2003
Same author

Ecologic versus individual-level sources of bias in ecologic estimates of contextual health effects.

International journal of epidemiology·2002
Same author

[Infantile leukemia and exposure to 50/60 Hz magnetic fields: review of epidemiologic evidence in 2000].

Annali dell'Istituto superiore di sanita·2002

Related Experiment Video

Updated: Jan 15, 2026

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling
08:26

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling

Published on: June 23, 2022

2.0K

Bias in the one-step method for pooling study results.

S Greenland1, A Salvan

  • 1Department of Epidemiology, UCLA School of Public Health 90024-1772.

Statistics in Medicine
|March 1, 1990
PubMed
Summary
This summary is machine-generated.

The one-step Peto method can produce biased pooled effect estimates, especially with unbalanced data. Alternative methods like Mantel-Haenszel or exact methods are recommended for reliable meta-analysis results.

More Related Videos

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

15.0K
Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI
06:26

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI

Published on: November 27, 2019

77.1K

Related Experiment Videos

Last Updated: Jan 15, 2026

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling
08:26

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling

Published on: June 23, 2022

2.0K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

15.0K
Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI
06:26

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI

Published on: November 27, 2019

77.1K

Area of Science:

  • Biostatistics
  • Epidemiology
  • Medical Research

Background:

  • The one-step (Peto) method is commonly used for pooled effect estimation in meta-analyses.
  • Unbalanced study data can significantly impact the reliability of the Peto method.
  • Bias in pooled estimates can lead to erroneous conclusions in research synthesis.

Purpose of the Study:

  • To evaluate the performance of the one-step (Peto) method for pooled effect estimation.
  • To identify potential biases associated with the Peto method, particularly in unbalanced data.
  • To recommend alternative meta-analysis methods that provide more accurate and reliable effect estimates.

Main Methods:

  • The study critically assessed the one-step (Peto) method.
  • Comparative analysis of Peto method against other established meta-analysis techniques.
  • Evaluation of bias in pooled effect estimates under varying data conditions (balanced vs. unbalanced).

Main Results:

  • The one-step (Peto) method demonstrated a tendency to yield extremely biased results with unbalanced data.
  • Even with balanced studies, the Peto method may introduce unacceptable levels of bias.
  • Alternative methods showed superior performance in providing unbiased pooled effect estimates.

Conclusions:

  • The one-step (Peto) method is not universally suitable for pooled effect estimation due to potential bias.
  • For adequate total events, ordinary Mantel-Haenszel, weighted least squares, or maximum likelihood estimates are preferred.
  • Exact methods are recommended for meta-analyses with a small total number of events.