# Guidelines

In this page, you will find a collection of flowcharts designed to help you choose which functions of Pingouin are adequate for your analysis. Click on the desired flowchart to view a full scale image with hyperlinks.

Table of Contents

## One-way ANOVA

### Example code

```
import pingouin as pg
# Load an example dataset comparing pain threshold as a function of hair color
df = pg.read_dataset('anova')
# 1. This is a between subject design, so the first step is to test for equality of variances
groups = df['Hair color'].unique()
a, b, c, d = [df.groupby('Hair color')['Pain threshold'].get_group(g).values for g in groups]
equal_var, pval = pg.homoscedasticity(a, b, c, d)
# 2. If the groups have equal variances, we can use a regular one-way ANOVA
pg.anova(data=df, dv='Pain threshold', between='Hair color')
# 3. If there is a main effect, we can proceed to post-hoc Tukey test
pg.pairwise_tukey(data=df, dv='Pain threshold', between='Hair color')
```

## Correlation

### Example code

```
import pingouin as pg
import seaborn as sns
# Load an example dataset with the personality scores of 500 participants
df = pg.read_dataset('pairwise_corr')
# 1.Test for bivariate normality
print(multivariate_normality(df[['Neuroticism', 'Openness']]))
# 1bis. Visual inspection with a histogram + scatter plot
sns.jointplot(data=df, x='Neuroticism', y='Openness', kind='reg')
# 2. If the data have a bivariate normal distribution and no clear outlier(s), we can use a regular Pearson correlation
pg.corr(df['Neuroticism'], df['Openness'], method='pearson')
```

## Non-parametric

### Example code

```
import pingouin as pg
# Load an example dataset comparing pain threshold as a function of hair color
df = pg.read_dataset('anova')
# There are 4 independent groups in our dataset, we'll therefore use the Kruskal-Wallis test:
pg.kruskal(data=df, dv='Pain threshold', between='Hair color')
```