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Nonparametric Tests NP |
 Duration : 0.5 day(s) | |
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:: Course SummaryThis training covers the fundamental statistical concepts involved in nonparametric statistical tests. These tests are used whenever the nature of the data requires it, such as rank data, or whenever the assumptions underlying the classical parametric methods, such as t-tests and ANOVA, are not likely to hold.:: Learning ObjectivesUpon completion of this course, participants will be able to:
Know the advantages and limitations of nonparametric tests
Assess when nonparametric tests should be used, based on the data and study objectives
Decide which nonparametric test to use for a given set of data
Perform the analysis
Interpret the results:: Target AudienceA statistical training session applied to all people who analyze data.:: PrerequisiteThis one-day workshop introduces the important ideas behind the most commonly used nonparametric tests. It assumed that participants have no previous knowledge of statistics or that they have not used it for a long time. | | |
:: Topics Covered
- Conditions for the Application of Parametric Tests
- Use of Nonparametric Tests
- Differences between Parametric and Nonparametric Tests
- Tests Adapted to the Various Experimental Designs
- Case of a Single Group: Sign Test, Wilcoxon
- Case of 2 Groups: Sign Test, Wilcoxon, Mann-Whitney vs. Student
- Case of More Than 2 Groups: Kruskal-Wallis vs. One-Way ANOVA
- Case of Randomized Complete Block Designs: Friedman vs. ANOVA
- Case of Balanced Incomplete Block Designs: Durbin Test vs. ANOVA
- Choosing the Right Nonparametric Test for the Right Data Set
- Applications and Case Studies
- Available Statistical Software
:: Course ContentThe use of nonparametric tests is often required when one of the three following cases arises:
Small sample sizes
The variables collected are not continuous in nature
The requirements of traditional methods, such as the assumption of normally distributed data, are not satisfied
Parametric tests rely on several assumptions regarding the distribution of the data, such as the normality of the distributions and the equality of variances. Nonparametric or distribution-free tests do not impose any conditions on the distribution of the data but, instead, employ methods based on ranking the data.
This training session covers the basic concepts underlying nonparametric tests, how they differ from parametric tests and how to perform them. An emphasis is put on the utility of nonparametric tests when the type of data collected require them or when the underlying assumptions of parametric tests, like t-tests and ANOVA are violated. Comparisons between parametric and nonparametric tests for several test situations are reviewed: one group, two groups, several groups, etc. The limitations and disadvantages of nonparametric tests, such as loss of power, are also discussed.
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Upcoming Public Sessions |
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Introduction to the Design of Experiments (DOE) |
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