Overview
The statistical significance level is the chance that a relationship was due to the chance of random sampling. Thus if a correlation is significant at the .05 level, this means there is 5% chance or less that a correlation as strong or stronger than the given one would result from an unusual random sampling of data when in fact the correlation was zero. There are many, many specific significance tests. Common tests are listed below, but in addition each statistical procedure has associated significance tests which are discussed in the respective sections dealing with each procedure.
- Parametric Tests. Parametric tests make distributional assumptions, particularly that samples are normally distributed. When these assumptions are met, parametric tests are more powerful than their non-parametric counterparts and thus are preferable.
- Nonparametric Tests. These tests do not assume the normal distribution.
- Chi-square tests of the significance of crosstabs and goodness-of-fit
- Fisher's exact test for 2-by-2 tables with a cell < 5
- One-Sample runs test of randomness
- One-Sample Kolmogorov-Smirnov goodness-of-fit test
- Tests for two independent samples: Mann-Whitney U, Wald-Wolfowitz runs, Kolmogorov-Smirnov Z, and Moses extreme reactions tests
- Tests for more than two independent samples: Kruskal-Wallis, median, and Jonckheere-Terpstra tests
- Tests for two dependent samples: McNemar, marginal homogeneity, Wilcoxon, and sign tests
- Tests for more than two dependent samples: Friedman, Kendall's W, and Cochran Q tests
- Resampling methods for nonparametric significance estimates
|
Contents
|