5 Questions
What is the main difference between nonparametric and parametric statistics?
Nonparametric statistics makes minimal assumptions about the underlying distribution of the data, while parametric statistics make specific assumptions about the data distribution.
When are nonparametric tests often used?
Nonparametric tests are often used when the assumptions of parametric tests are evidently violated.
What kind of populations are non-parametric methods widely used for studying?
Non-parametric methods are widely used for studying populations that have a ranked order, such as movie reviews receiving one to four 'stars'.
What type of data do non-parametric methods result in, in terms of levels of measurement?
Non-parametric methods result in ordinal data.
Why are non-parametric methods considered more general and more robust than parametric methods?
Non-parametric methods make fewer assumptions, resulting in their applicability being much more general, and they are more robust due to the reliance on fewer assumptions.
Study Notes
Comparing Nonparametric and Parametric Statistics
- The main difference between nonparametric and parametric statistics lies in the assumption about the distribution of the data: parametric statistics assumes a specific distribution (e.g. normality), while nonparametric statistics makes no assumption about the distribution.
When to Use Nonparametric Tests
- Nonparametric tests are often used when the normality assumption of parametric tests is not met, or when the sample size is small.
Populations Suitable for Nonparametric Methods
- Non-parametric methods are widely used for studying populations that are non-normal, skewed, or have outliers.
Levels of Measurement in Nonparametric Methods
- Non-parametric methods result in ordinal or categorical data, which can be measured at the nominal or ordinal level.
Advantages of Nonparametric Methods
- Non-parametric methods are considered more general and robust than parametric methods because they don't require a specific distribution and are less sensitive to outliers or non-normality, making them more flexible and reliable in many situations.
Test your knowledge of nonparametric statistics with this quiz. Learn about statistical analysis with minimal assumptions about data distribution and its use for descriptive statistics or statistical inference.
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