# Meaning of parametric and non parametric test

Parametric tests are statistical tests that make specific assumptions about the underlying distribution of the data, such as normality and homogeneity of variance. They typically involve parameters that define the population distribution, like means and variances. Examples include t-tests, ANOVA, and linear regression.

Non-parametric tests, on the other hand, do not assume any specific distribution for the data. They are based on ranks or orders of data points and are often used when data does not meet the assumptions of parametric tests. Examples include the Mann-Whitney U test, Kruskal-Wallis test, and Wilcoxon signed-rank test.

Dichotomy: A categorical variable with only two categories- i, e. Male/Female Categorical Variable: A nominal variable on which positions and scores are not recorded a

numbers.

Scores: Any position on a numerical variable

Skewness of a Distribution: This is a distribution having a longer tail at one end than at th other. It is an asymmetrical distribution.

Kurtosis. This is the extent of peakedness in a distribution Normal Distribution: This is a symmetrical distribution having its mean, mode and Media equal. Also, the frequencies of the variable extend equally both to the left and to the right o

the mode.

Parametric Tests: These are tests whose efficacy tests whether the variable being studied at least approximately normally distributed. Non-Parametric Tests: These tests are developed without reference to the distribution variables.

a variable

f: frequency of occurrence or observations

n: the sample size in the number of observations selected from a population (number Occurrence

N=F: total number of observations comprising a population of interest.

Σ

or

pronounced as sigma i. e. is a summation sign which instructs us to “take the sum

add

Σ (2,4,6) = 10

square root sign directs us to find the square root of a number i e

√36

= this directs us to raise a quantity to the indicated power.

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1. Parametric tests assume specific assumptions about the underlying __________ of the data.

a) Patterns

b) Distribution

c) Relationships

2. Non-parametric tests are based on ranks or orders of _________ points.

a) Data

b) Hypotheses

c) Parameters

3. Parametric tests typically involve parameters that define the population __________.

a) Average

b) Distribution

c) Range

4. Non-parametric tests are often used when data does not meet the assumptions of __________ tests.

a) Hypothesis

b) Parametric

c) Control

5. A common parametric test used to compare means of two independent groups is the _________ test.

a) Mann-Whitney U

b) Kruskal-Wallis

c) t-test

6. In parametric tests, data is assumed to follow a specific ___________.

a) Rank

b) Distribution

c) Order

7. Non-parametric tests do not assume any specific data ___________.

a) Order

b) Distribution

c) Parameter

8. ANOVA is an example of a parametric test used to compare means of __________ groups.

a) Independent

b) Non-parametric

c) Categorical

9. Non-parametric tests are especially useful when dealing with ________ data.

a) Normally distributed

b) Categorical

c) Continuous

10. The Wilcoxon signed-rank test is a non-parametric test used to compare ____________.

a) Two independent groups

b) Paired samples

c) Multiple groups

11. Linear regression is an example of a parametric test used to model ___________.

a) Trends

b) Ranks

c) Categorical data

12. Non-parametric tests are less sensitive to outliers compared to __________ tests.

a) Independent

b) Parametric

c) Distributional

13. The distributional assumptions of parametric tests are related to ___________ and homogeneity of variance.

a) Normality

b) Ranks

c) Skewness

14. Non-parametric tests can be used for data that is measured on at least a(n) ___________ scale.

a) Nominal

b) Ordinal

c) Interval

15. In parametric tests, the assumptions need to be checked to ensure the ___________ of the results.

a) Robustness

b) Linearity

c) Validity