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

Introduction to Nonparametric Statistics01:28

Introduction to Nonparametric Statistics

1.5K
Nonparametric statistics offer a powerful alternative to traditional parametric methods, useful when assumptions about the population distribution cannot be made. Unlike parametric tests, which require data to follow a specific distribution with well-defined parameters (such as the mean and standard deviation), nonparametric tests do not require such constraints. This makes them particularly valuable when dealing with small sample sizes, skewed data, or ordinal and categorical variables.
One of...
1.5K
Introduction to Statistics01:17

Introduction to Statistics

66.7K
The science of statistics involves collecting, analyzing, interpreting, and presenting data. The method of collecting, organizing, and summarizing data is called descriptive statistics. The systematic method of drawing inferences from the sample data and predicting unknown characteristics of a population is called inferential statistics.
In statistics, the collection of individuals or objects under study is called population. The idea of sampling is to select a portion of the larger population...
66.7K
Biostatistics: Overview01:20

Biostatistics: Overview

945
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
945
Probability in Statistics01:14

Probability in Statistics

24.1K
Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
24.1K
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

16.7K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
16.7K
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

How Novices Interpret Generalizations and How Experts Use Them.

Cognitive science·2026
Same author

Hierarchical Bayesian estimation for cognitive models using Particle Metropolis within Gibbs (PMwG): A tutorial.

Behavior research methods·2025
Same author

Media coverage of climate activist groups in Germany.

Climatic change·2025
Same author

Expressing intentions is not climate action.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Comparing Likert and visual analogue scales in ecological momentary assessment.

Behavior research methods·2025
Same author

Out of the labs and into the streets: Effects of climate protests by environmental scientists.

Royal Society open science·2025

Related Experiment Video

Updated: Feb 27, 2026

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

How to become a Bayesian in eight easy steps: An annotated reading list.

Alexander Etz1, Quentin F Gronau2, Fabian Dablander3

  • 1University of California, Irvine, Irvine, CA, USA.

Psychonomic Bulletin & Review
|June 30, 2017
PubMed
Summary

This guide offers a concise introduction to Bayesian data analysis for new researchers. It provides a curated reading list covering theoretical and practical aspects of Bayesian statistics in behavioral and social sciences.

Keywords:
Bayesian statisticsHypothesis testing

More Related Videos

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

36.2K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

12.3K

Related Experiment Videos

Last Updated: Feb 27, 2026

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
A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

36.2K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

12.3K

Area of Science:

  • Psychology and related behavioral and social sciences.

Background:

  • Bayesian data analysis is an increasingly important statistical framework.
  • Researchers new to Bayesian statistics require accessible introductory resources.

Purpose of the Study:

  • To provide a curated reading list for a concise introduction to Bayesian data analysis.
  • To equip researchers with foundational knowledge of Bayesian methods for behavioral and social sciences.

Main Methods:

  • Compilation of eight recommended sources covering theoretical and practical Bayesian statistics.
  • Inclusion of 32 additional articles and books for background knowledge.
  • Resources are ordered incrementally from theory to application.

Main Results:

  • The guide offers a low time-commitment starting point for understanding Bayesian analysis.
  • Readers will gain an understanding of the principles and application of Bayesian methods.

Conclusions:

  • This reading list serves as an effective introduction to Bayesian data analysis for researchers.
  • The guide empowers researchers to understand and evaluate Bayesian methods in their work.