The document discusses the parametric student’s t-test, which is a statistical tool for comparing the means of two groups to determine if they are significantly different. It outlines the definition, types (one-sample, unpaired two-sample, paired t-test), and requirements (normal distribution, interval/ratio data) for conducting the test, as well as the importance of transformation for non-normal data. The content emphasizes the need for correct application and understanding of the test's assumptions for accurate results.

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
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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RESTRICTIONS AND CAUTIONS

12. THANK YOU
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