Related Experiment Video
Updated: Jul 8, 2026
12:39
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
Published on: December 10, 2012
Advantages masquerading as "issues" in Bayesian hypothesis testing: A commentary on Tendeiro and Kiers (2019)
Don van Ravenzwaaij1, Eric-Jan Wagenmakers1
1Department of Psychology.
Psychological Methods
|December 9, 2021
Summary
This study argues Null Hypothesis Bayesian Testing (NHBT) offers advantages over p-values, contrary to some critiques. NHBT, using Bayes factors, provides a valuable tool for statistical evidence and knowledge updating in research.
Area of Science:
- Statistics
- Bayesian Inference
- Hypothesis Testing
Background:
- Null Hypothesis Bayesian Testing (NHBT) and Bayes factors are presented as alternatives to frequentist p-values.
- A critique by Tendeiro and Kiers identified 11 issues with NHBT.
- The current work engages with this critique, offering a counter-perspective.
Purpose of the Study:
- To re-evaluate the identified issues of NHBT, arguing some are advantages.
- To clarify the roles of hypothesis testing versus parameter estimation in statistical analysis.
- To discuss the utility of Bayes factors in contrast to full posterior distribution estimation.
Main Methods:
- Scholarly critique and re-interpretation of existing arguments on NHBT.
- Use of concrete examples to illustrate statistical concepts.
- Critical discussion of specific recommendations regarding statistical reporting.
Main Results:
- Several issues raised against NHBT are reframed as significant advantages.
- The complementary nature of hypothesis testing and parameter estimation is highlighted.
- The value of Bayes factors for quantifying statistical evidence is defended.
Conclusions:
- NHBT represents a valuable improvement over traditional frequentist methods.
- Statistical thinking and evidence quantification are emphasized over rigid procedures.
- Bayes factors offer a more complete picture than previously suggested, complementing parameter estimation.
Related Concept Videos
01:16Null and Alternative Hypotheses
The actual hypothesis testing begins by considering two hypotheses. They are termed the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the population that is...
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the population that is...
01:11Types of Hypothesis Testing
There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p ≠ 0.5.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p ≠ 0.5.
01:14Errors In Hypothesis Tests
When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
01:16Statistical Hypothesis Testing
Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
01:13Accuracy and Errors in Hypothesis Testing
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
01:16Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...