# Guidelines

## One-way ANOVA

### Example code

import pingouin as pg

# Load an example dataset comparing pain threshold as a function of hair color

# 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

# 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