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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...
Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
Spearman's test calculates correlation by...
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.
Correlation and Regression00:53

Correlation and Regression

In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a negative...
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...

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A default Bayesian hypothesis test for correlations and partial correlations.

Ruud Wetzels1, Eric-Jan Wagenmakers

  • 1Department of Psychology, University of Amsterdam, Weesperplein 4, 1018 XA, Amsterdam, The Netherlands. wetzels.ruud@gmail.com

Psychonomic Bulletin & Review
|July 17, 2012
PubMed
Summary
This summary is machine-generated.

We introduce a default Bayesian hypothesis test for correlations. This method quantifies evidence for the null hypothesis and allows for sequential monitoring of results, offering advantages over traditional frequentist approaches.

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Area of Science:

  • Statistics
  • Psychological Research Methods

Background:

  • Frequentist hypothesis tests for correlations lack the ability to quantify evidence in favor of the null hypothesis.
  • Standard frequentist tests do not allow for sequential monitoring of results as data accumulate.

Purpose of the Study:

  • To propose a default Bayesian hypothesis test for detecting correlations and partial correlations.
  • To offer a Bayesian alternative to frequentist correlation tests with practical advantages.

Main Methods:

  • The proposed test is a direct application of Bayesian variable selection techniques within regression models.
  • The methodology is illustrated using three real-world examples from psychological research.

Main Results:

  • The Bayesian test provides a measure of evidence supporting the null hypothesis.
  • Researchers can monitor the test outcomes sequentially as data become available.

Conclusions:

  • The default Bayesian hypothesis test for correlations is easy to implement.
  • This Bayesian approach offers significant practical advantages over conventional frequentist methods in statistical analysis.