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Parametric vs Non-parametric Tests
Parametric and non-parametric tests are two different types of statistical tests. These tests assume that the data is from a population that is normally distributed, while non-parametric tests do not make this assumption. This means that parametric tests are more powerful than non-parametric tests when the data is actually from a normal distribution, but non-parametric tests are more powerful when the data is not from a normal distribution. Difference Between Parametric and Non-Parametric Test.
What is a Parametric Test?
A parametric test is a statistical test that uses information from the population distribution to calculate the probability that the difference between the sample means is due to chance.
What is a Non-Parametric Test?
A non-parametric test is a statistical test that does not require the assumption of a normal distribution. Non-parametric tests are typically used to compare two or more groups.
Differences Between The Parametric Test and The Non-Parametric Test
The parametric test is a statistical test that is used to test the hypothesis that the means of two populations are equal. The non-parametric test is a statistical test that is used to test the hypothesis that the distributions of two populations are equal.
Advantages and Disadvantages of Parametric and Nonparametric Tests
Parametric tests are more powerful than nonparametric tests. This `tests are more likely to find a difference between groups if one exists, while nonparametric tests are more likely to find a difference if the groups are very different from each other. Parametric tests are also more likely to produce results that are statistically significant, meaning that the difference between the groups is likely not due to chance.
However, parametric tests are also more sensitive to violations of the assumptions that underlie them. If the data do not meet the assumptions, the results of the test may be inaccurate. Nonparametric tests are less sensitive to violations of the assumptions, and are therefore less likely to produce inaccurate results.
Related Pairs of Parametric Test and Non-Parametric Tests
Parametric: t-test, ANOVA
Non-Parametric: Mann-Whitney U-test, Kruskal-Wallis test
Classification
Parametric Test: A parametric test is a statistical hypothesis test in which the underlying distribution of the sample is assumed to be known. Non-Parametric Test: A non-parametric test is a statistical hypothesis test in which the underlying distribution of the sample is not assumed to be known.
Types Of Non-Parametric Test
There are six types of non-parametric test. The six types of non-parametric test are as follows:
1. Chi-square Test
2. Mann-Whitney U Test
3. Wilcoxon Signed Rank Test
4. Kruskal-Wallis Test
5. Friedman Test
6. Spearman Rank Correlation Coefficient
Applications Of Parametric Tests
Some of the most common applications of these tests are:
1. To compare the means of two or more groups
2. To compare the proportions of two or more groups
3. To test for differences in variability between two or more groups
4. To test for differences in correlation between two or more groups
Applications Of Non-Parametric Tests
There are many applications of non-parametric tests. A few examples include:
1. To compare the average number of eggs laid by two groups of hens
2. To compare the average weight of two groups of mice
3. To compare the average number of words remembered by two groups of students
4. To compare the average size of tumors in two groups of rats
Difference Between Parametric and Non-Parametric Test.
