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Related Concept Videos

Statistical Hypothesis Testing01:16

Statistical 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...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

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

Decision Making: P-value Method

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 have a...
Types of Hypothesis Testing01:11

Types 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.
Behrens&#8211;Fisher Test00:57

Behrens–Fisher Test

The Behrens-Fisher test is a statistical method designed to address the Behrens-Fisher problem, which arises when comparing the means of two normally distributed populations with unequal variances. Unlike the Student's t-test, which assumes equal variances, the Behrens-Fisher test allows for mean comparison without this restrictive assumption. This flexibility makes it particularly valuable in scenarios where two independent samples exhibit normality but lack variance homogeneity.
This test is...

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Bayesian hypothesis testing for psychologists: a tutorial on the Savage-Dickey method.

Eric-Jan Wagenmakers1, Tom Lodewyckx, Himanshu Kuriyal

  • 1University of Amsterdam, Department of Psychology, Roetersstraat 15, 1018 WB Amsterdam, The Netherlands. EJ.Wagenmakers@gmail.com

Cognitive Psychology
|January 13, 2010
PubMed
Summary
This summary is machine-generated.

Bayesian hypothesis tests offer better evidence than p-value tests in cognitive psychology. The Savage-Dickey method provides a simpler way to compute these Bayesian tests for nested models.

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Area of Science:

  • Cognitive Psychology
  • Statistical Methods
  • Bayesian Inference

Background:

  • P-value hypothesis testing is standard in cognitive psychology but offers limited evidence assessment.
  • P-values provide a rough estimate of experimental effect evidence, not a precise measure.
  • Bayesian hypothesis tests offer a more appropriate measure of evidence by favoring models with higher average likelihood.

Purpose of the Study:

  • To introduce the Savage-Dickey density ratio method for computing Bayesian hypothesis tests.
  • To address the computational complexity often associated with Bayesian hypothesis testing.
  • To demonstrate a practical and flexible approach for Bayesian model comparison.

Main Methods:

  • The Savage-Dickey density ratio method is presented for Bayesian hypothesis testing.
  • This method is applicable to nested models with specific prior restrictions.
  • The approach bypasses the need for complex numerical computations.

Main Results:

  • The Savage-Dickey method provides a valid and flexible way to compute Bayesian hypothesis test results.
  • Practical examples confirm the method's utility across various scenarios.
  • The method simplifies the application of Bayesian hypothesis testing for nested models.

Conclusions:

  • The Savage-Dickey density ratio method offers a computationally efficient alternative for Bayesian hypothesis testing.
  • This method enhances the accessibility and application of Bayesian inference in cognitive psychology.
  • Researchers can more readily compare nested models using this simplified Bayesian approach.