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What is it: Nonparametric Tests are often used in place of their parametric counterparts when certain assumptions about the underlying population are questionable. For example, when comparing two independent samples, the Wilcoxon Mann-Whitney test does not assume that the difference between the samples is normally distributed whereas its parametric counterpart, the two sample t-test does. Nonparametric tests may be, and often are, more powerful in detecting population differences when certain assumptions are not satisfied. All tests involving ranked data, i.e. data that can be put in order, are nonparametric. Examples of Nonparametric Tests are as follows:

  1. Sign Test - Performs a one sample sign test of the median and calculates the corresponding point estimate and confidence interval.
  2. Runs Test - Test whether or not the data order is random.
  3. Mann-Whitney U Test - Performs a hypothesis test of the equality of population medians and calculates the corresponding point estimate and confidence interval.
  4. Wilcoxon's Signed Rank Test - Performs a one sample Wilcoxon signed rank test of the median and calculates the corresponding point estimate and confidence interval.
  5. Kruskal-Wallis Test - Kruskal Wallis performs a hypothesis test of the equality of population medians for a one-way design (two or more populations). This test is a generalization of the procedure used by the Mann-Whitney test.
  6. Friedman Test - Performs a non-parametric analysis of a randomized block experiment. Randomized block experiments are a generalization of paired experiments. The Friedman test is a generalization of the paired sign test with a null hypothesis of treatments having no effect. This test requires exactly one observation per treatment block combination.
  7. Spearman Rank Correlation Coefficient Test

When to use it: Use Nonparametric Tests when any of the following are true:

  1. The data are counts or frequencies of different types of outcomes.
  2. The data are measured on a nominal scale.
  3. The data are measured on an ordinal scale.
  4. The assumptions required for the validity of the corresponding parametric procedure are not met or cannot be verified
  5. The sample size is small.
  6. The measurements are imprecise.
  7. There are outliers and / or extreme values in the data, making the median more representative than the mean.

  Name
Format
Preview (Click to enlarge)
  Sign Test Template
Microsoft Excel Format
Microsoft Excel
Format
Sign Test Template
  Sign Test Completed Example
Microsoft Excel Format
Microsoft Excel
Format
Sign Test Completed Example
  Runs Test Template
Microsoft Excel Format
Microsoft Excel
Format
Runs Test Template
  Runs Test Completed Example
Microsoft Excel Format
Microsoft Excel
Format
Runs Test Completed Example
  Mann-Whitney U Test Template
Microsoft Excel Format
Microsoft Excel
Format
Mann-Whitney U Test Template
  Mann-Whitney U Test Completed Example
Microsoft Excel Format
Microsoft Excel
Format
Mann-Whitney U Test Completed Example
  Wilcoxon's Signed Rank Test Template
Microsoft Excel Format
Microsoft Excel
Format
Wilcoxon's Signed Rank Test Template
  Wilcoxon's Signed Rank Test Completed Example
Microsoft Excel Format
Microsoft Excel
Format
Wilcoxon's Signed Rank Test Completed Example
  Kruskal-Wallis Test Template
Microsoft Excel Format
Microsoft Excel
Format
Kruskal-Wallis Test Template
  Kruskal-Wallis Test Completed Example
Microsoft Excel Format
Microsoft Excel
Format
Kruskal-Wallis Test Completed Example
  Friedman Test Template
Microsoft Excel Format
Microsoft Excel
Format
Friedman Test Template
  Friedman Test Completed Example
Microsoft Excel Format
Microsoft Excel
Format
Friedman Test Completed Example
  Spearman Rank Correlation Coefficient Template
Microsoft Excel Format
Microsoft Excel
Format
Spearman Rank Correlation Coefficient Template
  Spearman Rank Correlation Coefficient Completed Example
Microsoft Excel Format
Microsoft Excel
Format
Spearman Rank Correlation Coefficient Completed Example
USD $19.95
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