1. A PowerPoint®-based guide to assist in choosing the suitable statistical test.
NOTE: This presentation has the main purpose to assist researchers and students in choosing the appropriate statistical test for studies that examine one variable (Univariate). Some multivariates analyses are also included.
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2. What do you want to do? For an answer, click on the button
I want to assess whether my data have a Normal distribution
I want to compare groups (Looking for differences between samples)
I want to make correlation or regression analysis between variables.
I want to check the replicability of data (analysis of random and systematic error)
I would choose the appropriate graph to my data.

3. You may choose the test according to sample size.
Tests for Data Analysis Distribution- Normality Normal distribution is requested when using continuos data and n<30
You may choose the test according to sample size.
Use D’Agostino, if n≥10
Use D’Agostino-Pearson, if n≥20
Use Lilliefors or Shapiro-Wilk,
for any n value
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4. What kind of data you have? (Click on the button).
Comparing groups (samples)
What kind of data you have? (Click on the button).
Parametric
(mean)
Ex: height / length / weight (Assuming a normal distribution on n>30)
NUMERICAL
Continuous
How to check Normality ?
Ordinal
Ex: Middle (1) / Moderate(2) Severe (3)
Nonparametric
Categorical data
Ex: Frequency: Yes / No Race
Gender
Nominal
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5. Numerical Data (parametric) If the distribution is not Normal, skip to "Abnormal"
How many groups (samples) do you have? 1 2
>2

6. Not sure? Dependent Samples mean: Left Side X Right Side T1 x T 2 x T3
Numerical Data (parametric) If the distribution is not Normal, skip to "Abnormal"
ABNORMAL
Are your samples paired or dependent? No
Yes
Not sure? Dependent Samples mean:
Before X After
Left Side X Right Side T1 x T 2 x T3

7. Numerical Data (parametric) If the distribution is not Normal, skip to "Abnormal"
Answer: one sample t test
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8. Answer: Independent t test
Numerical Data (parametric) If the distribution is not Normal, skip to "Abnormal"
ABNORMAL
Answer: Independent t test
or ANOVA.
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9. Numerical Data (parametric) If the distribution is not Normal, skip to "Abnormal"
Answer: Paired t test or
ANOVA for repeated measurements. .
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10. Numerical Data (parametric) If the distribution is not Normal, skip to "Abnormal"
Answer: Analysis of Variance (ANOVA)
or MANOVA (Multiple Analysis of Variance),
if you have >1 variable.
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11. Ordinal Categorical Data (Nonparametric)
How many groups (samples) do you have ? 2
>2

12. Ordinal Categorical Data (Nonparametric)
Are your samples paired or dependent? No
Yes
Not sure? Dependent Samples mean:
Before X After
Left Side X Right Side T1 x T 2 x T3

13. Ordinal Categorical Data (Nonparametric)
Are your samples paired or dependent? No
Yes
Not sure? Dependent Samples mean:
Before X After
Left Side X Right Side T1 x T 2 x T3

14. Ordinal Categorical Data (Nonparametric)
Answer: Mann-Whitney test
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15. Ordinal Categorical Data (Nonparametric)
Answer: Wilcoxon (signed rank test)
or Signal test.
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16. Ordinal Categorical Data (Nonparametric)
Answer: Kruskal-Wallis’ Test
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17. Ordinal Categorical Data (Nonparametric)
Answer: Friedman’s Test
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18. Nominal Categorical Data (Nonparametric)
How many groups (samples) do you have ? 2
>2

19. Nominal Categorical Data (Nonparametric)
Are your samples paired or dependent? No
Yes
Not sure? Dependent Samples mean:
Before X After
Left Side X Right Side T1 x T 2 x T3

20. Nominal Categorical Data (Nonparametric)
Is there any expected value <5 ? No
Yes
Not sure? If some of the cells in the contingency table give values (expected) lower than 5.

21. Nominal Categorical Data (Nonparametric)
Are your samples paired or dependent? No
Yes
Not sure? Dependent Samples mean:
Before X After
Left Side X Right Side T1 x T 2 x T3

22. Nominal Categorical Data (Nonparametric)
Answer: Chi-square (x²) test or
Binomial Test, if using 2 samples and
proportion (%)
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23. Nominal Categorical Data (Nonparametric)
Answer: Cochran’s test
(absolute or relative frequence: %)
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24. Nominal Categorical Data (Nonparametric)
Answer: McNemar’s test
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25. Nominal Categorical Data (Nonparametric)
Answer: Exact Fisher’s test
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26. Correlation or Regression Analysis
What kind of data you have? (Click on the button).
Parametric
(mean)
Numerical
Ex: height / length / weight (Assuming a normal distribution)
How to check Normality ?
Ordinals
Ex: Middle (1) / Moderate(2) Severe (3)
Nonparametric
Categorical data
Nominal
Ex: Frequency: Yes / No Race
Gender

27. Numerical Data (parametric) If the distribution is not Normal, skip to "Abnormal"
How many variables do you have? 2
>2

28. Answer: Pearson’s Correlation Simple Linear Regression
Correlation tests or regression analysis to Continuos data If the distribution is not Normal, skip to "Abnormal"
ABNORMAL
Answer: Pearson’s Correlation
Simple Linear Regression
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29. Correlation tests or regression analysis to Continuos data If the distribution is not Normal, skip to "Abnormal"
ABNORMAL
Answer: Pearson’s Correlation (parcial) or Canonical Correlation
Multiple Linear Regression
NOTE: For Correlation all variables examined must have a Normal Distribution.
For Linear Regression dependent variable must have a Normal Distribution
How to check Normality ?
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30. Correlation test to Ordinal data (nonparametric)
Answer : Spearman or Kendal Correlation
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31. Correlation and Regression Analysis to Nominal data (nonparametric)
How many variables do you have? 2
>2

32. Correlation test to Nominal data (nonparametric)
Answer: Contingency coefficient C
Simple Logistic Regression
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33. Correlation test to Nominal data (nonparametric)
Answer: Contingency coefficient C
Multiple Logistic Regression
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34. Replicability or Reproducibility (Systematic error)
What kind of data you have? (Click on the button).
Parametric
(mean)
Numerical
Ex: height / length / weight (Assuming a normal distribution)
How to check Normality ?
Ordinal
Ex: Middle (1) / Moderate(2) Severe (3)
Nonparametric
Categorical data
Nominal
Ex: Frequency: Yes / No Race
Gender

35. Replicability or Reproducibility (Systematic error for numerical data)
ABNORMAL
Answer: Parametric test for dependent data
Note: Intraclass correlation can be used, if you would like to check the association between 2 or more measurements.
2 samples
>2 samples
For random or casual error , you may use
TEM (technical error measurement):
D= difference between repeated measures
n=number of individuals
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36. Replicability or Reproducibility (Systematic error for ordinal data)
Answer: Weighted Kappa
NOTE: in case of an ordinal variable, nonparametric tests for paired or dependent data can also be used
2 sample
> 2 samples
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37. Replicability or Reproducibility (Systematic error for Nominal data)
Answer: Kappa
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38. What kind of data you have? (Click on the button).
Graph Selection
What kind of data you have? (Click on the button).
Parametric
(mean)
Numerical
Ex: height / length / weight (Assuming a normal distribution)
Ordinal
Ex: Middle (1) / Moderate(2) Severe (3)
Nonparametric
Categorical data
Ex: Frequency: Yes / No Race
Gender
Nominal
Back to beginning

39. Not sure? Dependent Samples mean: Left Side X Right Side T1 x T 2 x T3
Graph Selection
Comparing Independent Samples
Comparing Dependent Samples (paired)
Making Data Correlation or regression
Not sure? Dependent Samples mean:
Before X After
Left Side X Right Side T1 x T 2 x T3

40. BOX-PLOT - Comparing Groups. Continuous or Ordinal Data (Score)
This chart describes the measure of central tendency (MEAN for continuos data or MEDIAN for Ordinal data), measures of dispersion (Standard deviation for parametric data or interquartiles deviation for Ordinal data) and the whiskers (maximum and minimum values )
Outlier: an observation that
is numerically distant
from the rest of the data.
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41. Line graph for longitudinal data
This chart describes the measure of central tendency (mean for Continuos data or median for Ordinal data) longitudinally
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42. Graphic for Correlation Tests or Regression Analysis
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43. Bar/Column Graphic Nominal data (frequency)
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