Concept explainers
l. Parametric tests (such as t or ANOVA) differ from nonparametric tests (such as chi-square) primarily in terms of the assumption they require and the data they use. Explain the differences.
The differences between parametric and non-parametric tests.
Answer to Problem 1P
Solution:
Non Parametric tests | Parametric tests |
No information about the population is available. | Information about the population is completely known. |
No assumptions are made regarding the population. | Specific assumptions are made regarding the population. |
The null hypothesis is free from parameters. | The null hypothesis is made on parameters of population distribution. |
Explanation of Solution
The basic difference between parametric and non-parametric tests are the assumptions associated with non-parametric tests. They are:
- The sample observations are independent.
- The variable under study is continuous.
- The probability density function is continuous.
- Lower order moments exist.
- The non-parametric tests do not require the population to have a normal distribution.
Conclusion:
These are the assumptions for non-parametric tests which are fewer and much weaker than those associated with parametric tests.
Justification:
This is why the non-parametric tests can be performed on data measured on any scale.
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