This document provides an introduction to parametric tests in statistics. It defines a parametric test as a statistical test that makes specific assumptions about the population parameter. For a test to be considered parametric, the data must meet four conditions: it must be on an interval or ratio scale, subjects must be randomly selected, and the data must be normally distributed. Examples of parametric tests include t-tests, ANOVA, and z-tests. Parametric tests are more powerful than non-parametric tests if their assumptions are met, but non-parametric tests can be used when the data does not meet parametric assumptions, such as with nominal scale data.

1. Welcome

2. Parametric test
Presented by
Romharsan poudel
Mphill schoolar
Roll No: 75155010(mathematics education)
I semester
Nepal open university
2075/04/29

3. Parametric test
A statistical test, in which specific assumptions are
made about the population parameter is known as
parametric test.

4. when to use parametric test?
To use parametric test; the following Four conditions
have to be satisfied:
-Data must be in Interval scale.
-Data must be in ratio scale.
-Subjects should be randomly selected.
_Data should be normally distributed.

5. Determination of parametric test
 Interval scale: interval between observation in terms
of fixed unit of measurement. Eg. Measures of
temperature.
 Ratio scale: The scale has a fundamental zero point.
Eg. Age, income.
(IN CASE OF NOMINAL AND ORDINAL SCALE NON-
PARAMETRIC TEST IS USED)

6. Parametric VS Non Parametric test
 .
PARAMETRIC •NON-PARAMETRIC
Specific assumptions are made
regarding the population
•No specific assumptions are made
regarding the population
Parametric test is powerful if it is exists •Not powerful like parametric test
Test statistics based on distribution •Test statistics is arbitrary
•No parametric test exists for nominal
scale data
•Central measure - mean
•Can draw more conclusions
•Non parametric test exists for nominal
scale data
•Central measure - median
•Simplicity , not affected by outliers

7. Contd.
Two sampled t-test Mann-whitney test
Paired t test Wilcoxon signed rank test
Anova Kruskal _wallis test

8. TYPES of parametric tests
1.Large samplel tests.

Z test
2. Small sample test
 t-test
 Independent /unpaired – t test
 Paired t-test
ANOVA(analysis of variance)
 one way analysis of variance
 Two way analysis of variance.

10. Thank you