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

Factorial Design02:01

Factorial Design

14.9K
Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
14.9K
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

1.2K
Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
1.2K
Experimental Designs01:16

Experimental Designs

18.4K
An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...
18.4K
Study Design in Statistics01:15

Study Design in Statistics

10.2K
A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
10.2K
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

334
Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
334
Probability Laws01:49

Probability Laws

44.7K
Overview
44.7K

You might also read

Related Articles

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

Sort by
Same author

Robust Bayesian multilevel meta-analysis: Adjusting for publication bias in the presence of dependent effect sizes.

Behavior research methods·2026
Same author

Investigating the replicability of the social and behavioural sciences.

Nature·2026
Same author

Investigating the analytical robustness of the social and behavioural sciences.

Nature·2026
Same author

Continuous outcome estimation in N-of-1 trials for accelerated decision-making.

Epilepsia·2026
Same author

To Be FAIR: Theory Specification Needs an Update.

Perspectives on psychological science : a journal of the Association for Psychological Science·2026
Same author

A tutorial on Bayesian hypothesis testing of correlation coefficients using the BFpack-module in JASP.

Behavior research methods·2025

Related Experiment Video

Updated: Mar 6, 2026

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

17.2K

Bayes factor design analysis: Planning for compelling evidence.

Felix D Schönbrodt1, Eric-Jan Wagenmakers2

  • 1Department of Psychology, Ludwig-Maximilians-Universität München, Leopoldstr. 13, 80802, München, Germany. felix@nicebread.de.

Psychonomic Bulletin & Review
|March 3, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces Bayes Factor Design Analysis (BFDA) for efficient experiment design using Bayes factors (BFs). It details three BF designs to maximize study informativeness and guide researchers in planning effective research.

Keywords:
Bayes factorDesign analysisDesign planningPower analysisSequential testing

More Related Videos

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

756
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.7K

Related Experiment Videos

Last Updated: Mar 6, 2026

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

17.2K
Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

756
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.7K

Area of Science:

  • Statistics
  • Psychology
  • Biostatistics

Background:

  • Frequentist power analysis is common for Null-Hypothesis Significance Testing (NHST) experiment design.
  • Literature on designing experiments using Bayes factors (BFs) as evidence measures is scarce.

Purpose of the Study:

  • To introduce Bayes Factor Design Analysis (BFDA) as a tool for efficient and informative study design.
  • To explore and evaluate different Bayes Factor (BF) study designs.

Main Methods:

  • Elaboration on three Bayes Factor (BF) designs: fixed-n, sequential (SBF), and modified sequential SBF.
  • Utilizing Monte Carlo simulations to evaluate design properties like expected evidence strength and sample size.

Main Results:

  • Demonstration of how to evaluate properties of different BF designs.
  • Providing researchers with methods to compute their own Bayesian design analyses.

Conclusions:

  • Bayes Factor Design Analysis (BFDA) offers a framework for optimizing experiment design.
  • The study equips researchers with tools to design more efficient and informative studies using Bayes factors (BFs).