The document provides an overview of choosing appropriate statistical tests based on the type of data (quantitative or qualitative) and distribution (normal or non-normal). It classifies tests into parametric and non-parametric, detailing specific tests like t-tests, ANOVA, and chi-square, along with their applicable data conditions. Key considerations include the type of analysis required, such as comparison or prediction, and whether the data sets are paired or unpaired.

1. CHOOSING A STATISTICAL
TEST
DR SYED ATA UL MUNAMM

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CC BY-SA-NC

4. Statistical tests are tools
They measure significance of your results

5. • P value <0.05(5%)
• 95% Confidence interval
• 2.5(1.5-5.5)
• An in many more ways

6. Classification of statistical test
• Paramteric tests
• Non parameteric tests

7. Parametric test
T test
Paired and
Unpaired
ANOVA
One way ANOVA
Repeated or two
way ANOVA
Linear
regression
Pearson
Correlation

8. Non
parametric
test
Wilconson
sign rank test
Chi square
Fischer exact
test
Logistic
regression
Non
parameteric
regression

9. Steps
• See what type of data are you measuring
Quantitative or qualitative
• Check Normality
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10. Continue..
• Type of analysis
Comparison/Difference
Association/ Relationship
Prediction
• Design
One group one data set
One group two data set or more(Paired)
Two group two data set or more(Unpaired)
More than two groups & data sets

12. Paired study Design

14. Type of
data&Distribution
Comparison Association/
Relationship
Prediction
2 data set >2 data set
Paired unpaired Paired unpaired
Continuous with
Normal
Distribution
(Mean)
Paired
t-test
Unpaired t
test
Two way
ANOVA
One way
ANOVA
Pearson
correlation
Linear
Regression
Ordinal
Non- Normal
Distribution
(Median)
Wilcoxon
signed
Rank
Wilcoxon
signed
Rank
Mann
whitening
‘U’ test
Friedman
test
Kruskal Wallis Spearman
correlation
Non Parametric
regression
Nominal
Dichotomous data
McNemar
test
Chi Square Cochran
Q test
Chi square Contingency
Coefficient
Logistic
regression

15. Choosing a Statistical test