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Related Concept Videos

Statistical Hypothesis Testing01:16

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The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...
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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.
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The Measurement and Treatment of Suppression in Amblyopia
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Three Approaches to Testing for Statistical Suppression.

Felix B Muniz1, David P MacKinnon2

  • 1Center for Indigenous Health, Johns Hopkins University.

Multivariate Behavioral Research
|May 21, 2025
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Summary
This summary is machine-generated.

This study compares three statistical tests for suppression effects, finding that the mediation test offers the best performance for identifying unexpected increases in effects when adjusted for third variables.

Keywords:
Suppressionmediationregression

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Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Quantitative Psychology

Background:

  • Suppression effects, where an effect unexpectedly increases after adjusting for a third variable, are crucial in theoretical and applied research.
  • Understanding and accurately testing for suppression effects is essential for robust statistical analysis.

Purpose of the Study:

  • To investigate and compare three distinct statistical approaches for testing suppression effects.
  • To evaluate the performance of these tests through simulation and real-world data analysis.

Main Methods:

  • Compared three tests for statistical suppression: one based on zero-order and semi-partial correlations (1978), another on a necessary condition for suppression (1997), and a third extending the inconsistent mediation test.
  • Derived standard errors for the Velicer, and Sharpe and Roberts tests.
  • Conducted a statistical simulation study and applied tests to real data sets and published correlation matrices.

Main Results:

  • The test based on inconsistent mediation demonstrated superior properties in the simulation study.
  • When applied to example data, all three tests yielded consistent results.
  • Analytical work identified conditions under which the tests produced conflicting outcomes.

Conclusions:

  • The mediation test for suppression, specifically evaluating the sign of the product of mediated and direct effects, exhibited the best overall performance.
  • Accurate identification of suppression effects is vital for advancing statistical understanding and application.