The document discusses how to choose the appropriate statistical test based on the characteristics of the data. It outlines several key considerations for selecting a test, including the number and type of variables, whether the data is paired or independent, and if the continuous variables follow a normal distribution. The document then describes many commonly used statistical tests for different types of comparisons, including onesample, bivariate, and multivariate tests. It emphasizes that the correct statistical test must be applied to ensure valid conclusions can be drawn from the data analysis.

1. How to choose a statistical test?
Dr. S. A. Rizwan, M.D.
Public Health Specialist
SBCM, Joint Program – Riyadh
Ministry of Health, Kingdom of Saudi Arabia

2. Learning objectives
Demystifying statistics! – Lecture 6 SBCM, Joint Program – RiyadhSBCM, Joint Program – Riyadh
• Name the various commonly used statistical tests
• Describe the preconditions to select a statistical test
• Apply the correct test for the problem at hand
• Interpret the conclusions of the test appropriately

3. Introduction
Demystifying statistics! – Lecture 6 SBCM, Joint Program – RiyadhSBCM, Joint Program – Riyadh
• We apply a statistical test to confirm if
our study results have arisen due to
chance or due to a true effect
• It is important to apply the correct test
for a valid conclusion
• A number of considerations are
required before selecting a test

4. Introduction
Demystifying statistics! – Lecture 6 SBCM, Joint Program – RiyadhSBCM, Joint Program – Riyadh
• There are more than 100s of statistical
tests available
• This book is a useful and simple guide
• Apart from tests there are many
statistical methods which are also
considered statistical tests

5. Prerequisites for deciding a test
Demystifying statistics! – Lecture 6 SBCM, Joint Program – RiyadhSBCM, Joint Program – Riyadh
• How many variables are there?
• What is the nature of dependent and
independent variable?
• How many categories are there in the
categorical variable?
• Does the continuous variable follow normal
distribution? (Parametric vs. Non parametric)
• Is there any pairing in the data/variables?
* Recall types of variable / data?

6. What is meant by data follows normal dist?
Demystifying statistics! – Lecture 6 SBCM, Joint Program – RiyadhSBCM, Joint Program – Riyadh
• As a general rule
• Small samples (<30) are not normal
• Conversely large (>30) samples are considered to
be normal
• Normal distribution can be checked
• Informally:
• Visual methods: histogram, P-P, Q-Q plots
• Comparing SD and mean
• Formally:
• Statistical tests (Kolmogorov–Smirnov, Shapiro–
Wilk)

7. Dependent / independent variable?
Demystifying statistics! – Lecture 6 SBCM, Joint Program – RiyadhSBCM, Joint Program – Riyadh

8. Dependent / independent variable?
Demystifying statistics! – Lecture 6 SBCM, Joint Program – RiyadhSBCM, Joint Program – Riyadh
• Height
• Weight
• Diet
• Socio-economic status
• Drug type
• Muscle mass
• Quality of life
• Wealth
• Employment
• Sex
• Treatment type
• Cure rate
• Survival index
• Maternal mortality rate
• Teenage pregnancy
• Immunisation coverage
• Education
• House type
• Health accessibility
• Mortality
• Tobacco use
• Domestic violence
* Can you classify these variables
into dependent and independent?

9. Paired / unpaired data?
Demystifying statistics! – Lecture 6 SBCM, Joint Program – RiyadhSBCM, Joint Program – Riyadh
• Paired data/samples in which
natural or matched couplings
occur
• Pre-test/post-test samples
• Cross-over trials
• Matched samples
• Cluster samples

10. How to choose a statistical test?
Demystifying statistics! – Lecture 6 SBCM, Joint Program – RiyadhSBCM, Joint Program – Riyadh

11. One sample tests
Demystifying statistics! – Lecture 6 SBCM, Joint Program – RiyadhSBCM, Joint Program – Riyadh
• One dependent variable compared to a
hypothesized population value
• There are no independent variables
• Rarely used in community medicine
Nature of variable Test name
Normal One sample t-test
Ordinal/interval One sample median
Categorical (2 levels) Binomial test
Categorical (>2 levels) Chi-square GOF
* If the sample size is large and within known population
parameters, Z test is used.
No independent variable

12. Comparison of 2 variables - 1
Demystifying statistics! – Lecture 6 SBCM, Joint Program – RiyadhSBCM, Joint Program – Riyadh
• One independent variable
• Categorical
• Independent
• 2 levels
• One dependent variable
• Some of the most commonly used tests
• Aka, bivariate analysis
Nature of DV Test name
Normal
Independent sample
t-test
Ordinal/interval
(not normal)
Wilcoxon-Mann
Whitney test
Categorical
(assumptions met)
Chi-square test
Categorical
(assumptions NOT met)
Fisher’s exact test

13. Comparison of 2 variables - 2
Demystifying statistics! – Lecture 6 SBCM, Joint Program – RiyadhSBCM, Joint Program – Riyadh
• One independent variable
• Categorical
• Independent
• >2 levels
• One dependent variable
• Some of the most commonly used tests
• Aka, bivariate analysis
Nature of DV Test name
Normal One way ANOVA
Ordinal/interval
(not normal)
Kruskal Wallis
Categorical
(assumptions met)
Chi-square test
Categorical
(assumptions NOT met)
Fisher’s exact test

14. Comparison of 2 variables - 3
Demystifying statistics! – Lecture 6 SBCM, Joint Program – RiyadhSBCM, Joint Program – Riyadh
• One independent variable
• Categorical
• Dependent
• 2 levels
• One dependent variable
Nature of DV Test name
Normal Paired t-test
Ordinal/interval
(not normal)
Wilcoxon signed
ranks test
Categorical McNemar

15. Comparison of 2 variables - 4
Demystifying statistics! – Lecture 6 SBCM, Joint Program – RiyadhSBCM, Joint Program – Riyadh
• One independent variable
• Categorical
• Dependent
• >2 levels
• One dependent variable
Nature of DV Test name
Normal
One-way repeated
measures ANOVA
Ordinal/interval
(not normal)
Friedman test
Categorical
Repeated measures
logistic regression

16. Comparison of 2 variables - 5
Demystifying statistics! – Lecture 6 SBCM, Joint Program – RiyadhSBCM, Joint Program – Riyadh
• One independent variable
• Interval
• One dependent variable
Nature of DV Test name
Normal
Correlation
Regression
Ordinal/interval
(not normal)
Non-parametric
correlation
Categorical
Simple logistic
regression

17. Other statistical methods - 1
Demystifying statistics! – Lecture 6 SBCM, Joint Program – RiyadhSBCM, Joint Program – Riyadh
• One or more independent variables
• Interval or categorical
• One dependent variable
Nature of DV Test name
Interval and Normal
Multiple regression
ANCOVA
Categorical
Multiple logistic
regression
Discriminant
analysis

18. Other statistical methods - 2
Demystifying statistics! – Lecture 6 SBCM, Joint Program – RiyadhSBCM, Joint Program – Riyadh
• One independent variable
• Independent groups
• 2 or more levels
• 2 or more dependent variables
Nature of DV Test name
Interval and Normal One way MANOVA

19. Demystifying statistics! – Lecture 6 SBCM, Joint Program – RiyadhSBCM, Joint Program – Riyadh
• 2 or more independent variables
• Categorical
• Independent
• One dependent variable
• Some of the most commonly used tests
• Aka, bivariate analysis
Nature of DV Test name
Normal Factorial ANOVA
Ordinal/interval
(not normal)
Ordered logistic
regression
Categorical
Factorial logistic
regression
Other statistical methods - 3

20. Other statistical methods - 4
Demystifying statistics! – Lecture 6 SBCM, Joint Program – RiyadhSBCM, Joint Program – Riyadh
• 2 or more independent variables
• 2 or more dependent variables
Nature of DV Test name
Interval and Normal
Multivariate
multiple linear
regression

21. Other statistical methods - 5
Demystifying statistics! – Lecture 6 SBCM, Joint Program – RiyadhSBCM, Joint Program – Riyadh
• 2 or more dependent variables
Nature of DV Test name
Interval and Normal
Canonical
correlation
Factor analysis

22. Summary – Tests you must know
Demystifying statistics! – Lecture 6 SBCM, Joint Program – RiyadhSBCM, Joint Program – Riyadh
Type of bivariate
comparison
Number
of
groups
Independent samples Paired samples
Parametric
(normal distribution)
Non-parametric
(non-normal distribution)
Parametric
(normal distribution)
Non-parametric
(non-normal distribution)
Categorical vs. categorical
(eg. Treatment type vs.
gender)
2
-
Chi square test
(or Fishers exact test)
-
McNemar test
Categorical vs. categorical
(eg. Disease severity vs.
religion)
>2
-
Chi square test
(or Fishers exact test)
- -
Categorical vs. quantitative
(eg. Two treatment types
vs. mean BP)
2
Students t test Wilcoxon rank sum test Paired t test Wilcoxon sign rank test
Categorical vs. quantitative
(eg. Three treatment type
vs. mean BP)
>2
One-way ANOVA (analysis
of variance)
Kruskal Wallis test
Repeated measures
ANOVA
Freidman’s test
Quantitative vs.
quantitative
(eg. Age vs. weight)
-
Pearson’s correlation
Regression analysis
Spearman’s rank
correlation Generalized linear models -

23. Summary
Demystifying statistics! – Lecture 6 SBCM, Joint Program – RiyadhSBCM, Joint Program – Riyadh

24. Take home messages
Demystifying statistics! – Lecture 6 SBCM, Joint Program – RiyadhSBCM, Joint Program – Riyadh
• A number of things must be considered
before selecting a test
• For a given test the assumptions must be
satisfied
• The results must be interpreted cautiously
with the context

25. Thank you!
Email your queries to sarizwan1986@outlook.com