Omnibus Statistical Test
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A Omnibus Statistical Test is a statistical test for examining the general statistical significance between the variance of population parameters.
- Example(s)
- Counter-Example(s):
- See: Analysis of Variance, Likelihood-Ratio test, Neyman-Pearson Lemma, Statistical Population Contrast.
References
2016
- (Wikipedia, 2016) ⇒ http://en.wikipedia.org/wiki/Omnibus_test Retrieved 2016-08-28
- Omnibus tests are a kind of statistical test. They test whether the explained variance in a set of data is significantly greater than the unexplained variance, overall. One example is the F-test in the analysis of variance. There can be legitimate significant effects within a model even if the omnibus test is not significant. For instance, in a model with two independent variables, if only one variable exerts a significant effect on the dependent variable and the other does not, then the omnibus test may be non-significant. This fact does not affect the conclusions that may be drawn from the one significant variable. In order to test effects within an omnibus test, researchers often use contrasts.
- In addition, Omnibus test as a general name refers to an overall or a global test. Other names include F-test or Chi-squared test.
- Omnibus test as a statistical test is implemented on an overall hypothesis that tends to find general significance between parameters' variance, while examining parameters of the same type [...]
- Omnibus tests commonly refers to either one of those statistical tests:
- ANOVA F test to test significance between all factor means and/or between their variances equality in Analysis of Variance procedure ;
- The omnibus multivariate F Test in ANOVA with repeated measures ;
- F test for equality/inequality of the regression coefficients in Multiple Regression;
- Chi-Square test for exploring significance differences between blocks of independent explanatory variables or their coefficients in a logistic regression.