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Related Concept Videos

Types of Hypothesis Testing01:11

Types of Hypothesis Testing

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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...
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Statistical Hypothesis Testing01:16

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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...
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Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

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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.
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Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

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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%...
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Bonferroni Test01:10

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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...
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Decision Making: P-value Method01:09

Decision Making: P-value Method

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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On Bayes factors for hypothesis tests.

Karl Christoph Klauer1, Constantin G Meyer-Grant2, David Kellen3

  • 1Department of Psychology, Albert-Ludwigs-Universität Freiburg, 79085, Freiburg, Germany. christoph.klauer@psychologie.uni-freiburg.de.

Psychonomic Bulletin & Review
|November 25, 2024
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Summary
This summary is machine-generated.

Researchers introduce new Bayes factors for statistical hypothesis testing, offering improvements over default methods for common analyses like t-tests and regression. These alternative Bayes factors are accessible via an R package.

Keywords:
Bayesian statisticsDefault Bayes factorsStatistical inference

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Area of Science:

  • Statistics
  • Psychological Research Methodology

Background:

  • Default Bayes factors are popular for hypothesis testing.
  • Certain features of default priors are considered implausible.
  • Existing methods may not fully address needs in psychological research.

Purpose of the Study:

  • Develop alternative Bayes factors for common statistical analyses.
  • Address limitations of default Bayes factors, particularly prior specifications.
  • Provide a computationally accessible tool for researchers.

Main Methods:

  • Derived alternative Bayes factors for one-sample and two-sample t-tests, regression, and ANOVA.
  • Utilized test-statistic-based Bayes factors framework.
  • Developed an R package for convenient computation.

Main Results:

  • Alternative Bayes factors demonstrate desirable theoretical and practical properties.
  • These factors mitigate implausible features of default priors.
  • Demonstrated equivalence between default/alternative Bayes factors and test-statistic-based Bayes factors.

Conclusions:

  • Alternative Bayes factors offer a robust alternative for hypothesis testing in psychological research.
  • Test-statistic-based Bayes factors provide a general computation approach.
  • The R package facilitates the adoption of these advanced statistical methods.