PARAMETRIC student test presentation (ppt)

A parametric test is a statistical test that makes assumptions about the parameters of the population distribution(s) from which one’s data is drawn.
APPLICATIONS
Used for Quantitative data.
Used for continuous variables.
Used when data are measured on approximate interval or ratio scales of measurement.
Data should follow normal distribution.

STUDENT’S - TEST
Student t test is a statistical test which is widely used to compare the mean of two groups of samples.


It is therefore to evaluate whether the means of the two sets of data are statistically significantly different from each other.

Developed by Prof. W. S. Gossett.

There are many types of t test:

1. The one-sample t-test, used to compare the mean of a
population with a theoretical value.

2. The unpaired two sample t-test, used to compare the mean of two independent samples.

3. The paired t-test, used to compare the means between two related groups of samples.

COMPUTATION OF T-TEST

Formula used is

t is the t-value,

X{1} and overline x{2} are the means of the two groups being compared,

s2 is the common variance of the two groups, and

n{1} and n{2} are the number of observations in each of the groups.

ONE - TAILED TEST

• In one-tailed test, the alternative hypothesis (HA) is that the mean of a particular nominated sample (A or B) will be greater than the mean of the other sample (B or A).

• The critical value of 't' is lower in one-tailed test.

TWO - TAILED TEST

In two-tailed test, the alternative hypothesis (HA) is that means of the two samples A and B are merely different.
Two-tailed test is more stringent and thus recommended.

The t-test assumes that data are measured at interval/ratio level.
Data should be derived from Normally distributed populations.
Counts data may not be Normally distributed, so count data should be transformed logarithmically before performing t-test.
Proportions and percentages data need to be arcsine transformed.

1. PARAMETRIC (STUDENT’S – T-TEST) PRIYADARSHINI J. L. COLLEGE OF PHARMACY, Electronic Zone Building , MIDC, Hingna Road Nagpur- 440016 2023-2024 1 Presented by Swapnil S. Tirmanwar

2. LEARNING OBJECTIVES 2 Content Definition of Parametric test Definition of t - test Computation of t – test One – tailed test Two – tailed test Restrictions and cautions

3. PARAMETRIC TEST A parametric test is a statistical test that makes assumptions about the parameters of the population distribution(s) from which one’s data is drawn. 3

4. Used for Quantitative data.
Used for continuous variables.
Used when data are measured on approximate interval or ratio scales of measurement. Data should follow normal distribution. 4 APPLICATIONS

5. STUDENT’S - TEST Student t test is a statistical test which is widely used to compare the mean of two groups of samples. It is therefore to evaluate whether the means of the two sets of data are statistically significantly different from each other. Developed by Prof. W. S. Gossett. 5

6. 6

7. There are many types of t test: 1. The one-sample t-test , used to compare the mean of a population with a theoretical value. 2. The unpaired two sample t-test , used to compare the mean of two independent samples. 3. The paired t-test, used to compare the means between two related groups of samples. 7

8. COMPUTATION OF T-TEST Formula used is

t is the t-value,

X{1} and overline x{2} are the means of the two groups being compared,

s2 is the common variance of the two groups, and

n{1} and n{2} are the number of observations in each of the groups. 8

9. ONE - TAILED TEST • In one-tailed test, the alternative hypothesis (HA) is that the mean of a particular nominated sample (A or B) will be greater than the mean of the other sample (B or A). • The critical value of 't' is lower in one-tailed test. 9

10. TWO - TAILED TEST In two-tailed test, the alternative hypothesis (HA) is that means of the two samples A and B are merely different. Two-tailed test is more stringent and thus recommended. 10

11. The t-test assumes that data are measured at interval/ratio level. Data should be derived from Normally distributed populations. Counts data may not be Normally distributed, so count data should be transformed logarithmically before performing t-test. Proportions and percentages data need to be arcsine transformed. 11 RESTRICTIONS AND CAUTIONS

12. THANK YOU 12