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**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. Please proceed to the next page ... If you have any suggestion, criticism, please contact the author by

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**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.

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**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 Back to beginning

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**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 Back to beginning

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**Numerical Data (parametric) If the distribution is not Normal, skip to "Abnormal"**

How many groups (samples) do you have? 1 2 >2

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**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

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**Numerical Data (parametric) If the distribution is not Normal, skip to "Abnormal"**

Answer: one sample t test Back to beginning

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**Answer: Independent t test**

Numerical Data (parametric) If the distribution is not Normal, skip to "Abnormal" ABNORMAL Answer: Independent t test or ANOVA. Back to beginning

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**Numerical Data (parametric) If the distribution is not Normal, skip to "Abnormal"**

Answer: Paired t test or ANOVA for repeated measurements. . Back to beginning

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**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. Back to beginning

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**Ordinal Categorical Data (Nonparametric)**

How many groups (samples) do you have ? 2 >2

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**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

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**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

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**Ordinal Categorical Data (Nonparametric)**

Answer: Mann-Whitney test Back to beginning

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**Ordinal Categorical Data (Nonparametric)**

Answer: Wilcoxon (signed rank test) or Signal test. Back to beginning

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**Ordinal Categorical Data (Nonparametric)**

Answer: Kruskal-Wallis’ Test Back to beginning

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**Ordinal Categorical Data (Nonparametric)**

Answer: Friedman’s Test Back to beginning

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**Nominal Categorical Data (Nonparametric)**

How many groups (samples) do you have ? 2 >2

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**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

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**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.

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**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

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**Nominal Categorical Data (Nonparametric)**

Answer: Chi-square (x²) test or Binomial Test, if using 2 samples and proportion (%) Back to beginning

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**Nominal Categorical Data (Nonparametric)**

Answer: Cochran’s test (absolute or relative frequence: %) Back to beginning

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**Nominal Categorical Data (Nonparametric)**

Answer: McNemar’s test Back to beginning

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**Nominal Categorical Data (Nonparametric)**

Answer: Exact Fisher’s test Back to beginning

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**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

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**Numerical Data (parametric) If the distribution is not Normal, skip to "Abnormal"**

How many variables do you have? 2 >2

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**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 Back to beginning

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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 ? Back to beginning

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**Correlation test to Ordinal data (nonparametric)**

Answer : Spearman or Kendal Correlation Back to beginning

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**Correlation and Regression Analysis to Nominal data (nonparametric)**

How many variables do you have? 2 >2

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**Correlation test to Nominal data (nonparametric)**

Answer: Contingency coefficient C Simple Logistic Regression Back to beginning

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**Correlation test to Nominal data (nonparametric)**

Answer: Contingency coefficient C Multiple Logistic Regression Back to beginning

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**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

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**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 Back to beginning

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**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 Back to beginning

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**Replicability or Reproducibility (Systematic error for Nominal data)**

Answer: Kappa Back to beginning

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**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

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**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

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**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. Back

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**Line graph for longitudinal data**

This chart describes the measure of central tendency (mean for Continuos data or median for Ordinal data) longitudinally Back

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**Graphic for Correlation Tests or Regression Analysis**

Back

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**Bar/Column Graphic Nominal data (frequency)**

Back

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