1. Commonly used statistical tests
in research
Dr Naqeeb Ullah Khan

2. Types of Statistics
 Descriptive statistics deals with enumeration,
organization and graphical representation of the data,
e.g. Census data.
 Inferential statistics is concerned with making
conclusions about population characteristics using
information contained in a sample, that is , generalizing
from the specific e.g. an opinion poll, such as the Gallop
Poll.

3. Population is the set of all measurements of
interest to the researcher.
Sample is any subset of measurements
selected from the population.
Population and Sample

4. Population and Sample
Population Sample

5. Examples of Population
1. Average Income of households in Pakistan.
2. Households in Islamabad who own at least
one car.
3. Patients coming to PIMS Hospital

6. Characteristic of Population and Sample
• Parameter: The set of measurements in a
population may be summarized by a
descriptive characteristic, called a parameter.
e.g. average income of household in Pakistan.
• Statistic: The set of measurements in a sample
may be summarized by a descriptive
characteristic, called a statistic. e.g. sample
mean

7. Measurement Scales
 Nominal Scale
Male-Female, Well-Sick, Child-Adult, Married-Unmarried
 Ordinal Scale
Unimproved, Improved, Much Improved.
Above average, average, below average etc.
 Intervals Scale
Arbitrary Zero and unit distance e.g. temperature.
 Ratio Scale
Height, weight and length etc.

8. Hypothesis Testing Decision Chart
Null Hypothesis (H0 ) is true
Alternative Hypothesis (H1)
is true
Reject (H0 )
Type I error
()
typically .05 or .01
Correct decision
(Power = 1 - )
typically .80
Don’t reject (H0 )
Correct decision
(1 - )
typically .95 or .99
Type II error
()
typically .20
Reality
Decision

9. 
1.96
Critical value
Region of nonrejection Region of rejection
Area=

10. Testing a mean 0 against a true alternative 1
0
Critical value
1
Region of nonrejection Region of rejection
Sampling distribution of
means when H0 is true
Sampling distribution of
means when H1 is true
Area=
Area=1-
Area=

12. Level of Measurement
Number of samples Nominal Ordinal Interval/Ratio
1 sample 2 test
Kolmogorov-Smirnoff 1
sample test
1 sample t-test
2 independent samples
2 test
Mann-Whitney U test 2 samples t-test
2 dependent samples McNemar test Wilcoxon test Paired t-test
>2 independent samples 2 test Kruskal-Wallis test ANOVA
>2 dependent samples Cochran Q test Friedman ranks test
Repeated
measures ANOVA
Some Commonly Used Statistical Tests

13. t-test
A test which compare means two 2 groups
When should the t-distribution be used?
When data variables are continuous
when sample size is small
when population SD is not known.
If you know population SD or your sample size exceeds
25, feel free to use the Standard Normal Distribution

14. Example: A gynecologist at PIMS feels that pregnant
women coming to the hospital are mostly anemic. She
takes a random sample of 25 pregnant women and test
their Hb level.
Is this average Hb level less than the Hb level of normal
women population? Which is 13.
H0: μ = 13 Ha: μ < 13
Significance level α = 0.5
Test Statistic: t= X- μ0
S⁄√n
X = 11.8 g/dl μ0=13
S = 2.6
t= 11.8 - 13 = -1.2 = -2.32
2.6⁄√25 0.52
-3 -2 -1 0 1 2 3
= 0.25-1.96

15. ANOVA
When there are more than 2 groups
then we use
ANOVA
(Analysis of Variances)

16. Multiple Comparison Test
After ANOVA test, to detect which group is
different, many types of test can be
performed.
Among these the most Powerful are
1. Duncan’s Test
2. Tukey’s HSD Test

17. MANOVA
When we are comparing more than
one variable in more than two groups
Than we use
MANOVA
(Multivariate Analysis of Variances)

18. ANCOVA
In ANOVA table when there is a
Co-Variant then we use
ANCOVA
(Analysis of Co Variants)

19. MANCOVA
In ANCOVA table when there are
more than one
Co-Variant then we use
MANCOVA
(Multivariate Analysis of Co Variants)

20. Chi-square Test
The chi-square test is the most
commonly used method for comparing
frequencies or proportions.
It is a statistical test used to determine if
observed data deviate from those
expected.
χ2 = Σ (O-E)2
E

21. Observed Expected Total
Heads 108 100 208
Tails 92 100 192
Total 200 200 400

22. Types of Chi-Square Test
• Chi Square Test Goodness-of-fit
• Chi Square Test of independence
• Chi Square Test of Homogeneity

23. Normal Distribution
4 5 6 7 8 9 10 11 12
8
7
6
5
4
3
2
1
Age groups in years
Frequency

24. Standard Deviation
It is the mean deviation of the data
points from the mean
and
by far the most useful measure of
variation

25. Normal Distribution Curve

26. Normal Distribution Curve

28. If the average height for adult men in Pakistan is
70” with a SD of 3”.
― 68% have a height within 67–73 inches
―95% have a height within 64–76 inches
―99.9% have a height within 61-79 inches
If the SD were 0, then all men would be exactly
70”high.

29. Example: Mean reaction time of a particular drug X
is 10 minutes with SD of 2 minutes .
1) Find 50th percentile reaction time.
Ans: 10 minutes
2) Find 99.9 Percentile reaction time.
X= 3.09 x 2 + 10 = 16.18 minutes
=10

30. Relationship Between Alpha(),
Sample Size (n), and Power (1-)
Sample Size per Group
20 40 60 80 100 120
Power
50
60
70
80
90
100
Two group t-test of equal means (equal n's)
Æ= 0.025 ( 2) Ê= 0.500
Æ= 0.050 ( 2) Ê= 0.500
Æ= 0.100 ( 2) Ê= 0.500α
α
α δ
δ
δ
n=51 n=64 n=78
power=.80

31. Example: The mean serum creatinine level measured in 36
patients 24 hours after they received a newly proposed
antibiotic was 1.2 mg/dl. If the mean and standard deviation of
serum creatinine in the general population are 1.0 and 0.4
mg/dl, respectively, then, using a significance level of 0.5, test if
the mean serum creatinine level in this group is different from
that of general population.
H0: μ = 1 Ha: μ ≠ 1
Significance level α = 0.5
Test Statistic: Z = X- μ0
σ⁄√n
X = 1.2 mg/dl σ = 0.4
Z = 1.2 - 1 = 0.2 x 6 = 3
0.4⁄√36 0.4
-3 -2 -1 0 1 2 3
1.96
= 0.25
-1.96
= 0.25

32. Without data, all you are just
another person with an opinion.
— Unknown
TheEND

33. Interval Scale Example