Distinguish between parametric and non-parametric test

Distinguish between parametric and non-parametric test

Parametric and non-parametric tests are two different categories of statistical tests used to analyze data. Here’s how they differ:

Parametric Tests:
1. Assumption: Parametric tests assume that the data follows a specific distribution, usually the normal distribution. This assumption is crucial for accurate results.
2. Measurement Level: Parametric tests are best suited for interval or ratio level data, where measurements have a meaningful zero point and consistent intervals.
3. Data Distribution: They require the data to have a specific distribution, such as a normal distribution, for accurate results.
4. Hypotheses: Parametric tests often involve comparing means or variances between groups or conditions.
5. Examples: t-tests, ANOVA, Pearson correlation, linear regression.

Non-parametric Tests:
1. Assumption: Non-parametric tests do not rely on assumptions about the distribution of the data. They are more robust and can be used when the distribution assumptions are not met.
2. Measurement Level: Non-parametric tests can be used with nominal, ordinal, interval, or ratio level data, making them more versatile.
3. Data Distribution: They do not assume a specific data distribution, which makes them suitable for skewed or non-normally distributed data.
4. Hypotheses: Non-parametric tests often involve comparing medians or assessing relationships between variables.
5. Examples: Mann-Whitney U test, Wilcoxon signed-rank test, Kruskal-Wallis test, Spearman’s rank correlation.

In summary, the choice between parametric and non-parametric tests depends on the nature of your data and whether the assumptions of parametric tests can be met. Parametric tests are more sensitive and powerful when assumptions are met, while non-parametric tests offer greater flexibility and robustness when assumptions are violated or when dealing with different types of data.

[mediator_tech]

1. Parametric tests assume a specific _______ distribution of data.
(a) normal
(b) exponential
(c) bimodal

2. Non-parametric tests are more suitable for _______ data.
(a) interval
(b) ordinal
(c) ratio

3. Parametric tests often require the data to follow a _______ distribution.
(a) uniform
(b) Poisson
(c) specific

4. Non-parametric tests are robust against violations of _______ assumptions.
(a) sample size
(b) distribution
(c) homoscedasticity

5. Parametric tests involve comparing _______ between groups or conditions.
(a) medians
(b) modes
(c) means

6. Non-parametric tests can be used when the data is not normally _______.
(a) skewed
(b) distributed
(c) continuous

7. Parametric tests are sensitive to _______ assumptions.
(a) distribution
(b) variance
(c) outlier

8. Non-parametric tests can be applied to _______ level data.
(a) nominal
(b) linear
(c) Gaussian

9. Parametric tests often yield more _______ results when assumptions are met.
(a) precise
(b) conservative
(c) biased

10. Non-parametric tests assess _______ relationships between variables.
(a) linear
(b) direct
(c) monotonic

11. Parametric tests have higher _______ when assumptions are satisfied.
(a) variability
(b) power
(c) complexity

12. Non-parametric tests include methods that use _______ instead of means.
(a) modes
(b) medians
(c) averages

13. Parametric tests are influenced by _______ outliers.
(a) extreme
(b) mild
(c) nominal

14. Non-parametric tests include the _______ test for two independent samples.
(a) ANOVA
(b) t-test
(c) Mann-Whitney U

15. Parametric tests are more appropriate when data follows a _______ distribution.
(a) exponential
(b) skewed
(c) bimodal