# Librairies
```python
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import scipy
from scipy.stats import ttest_1samp
```
# Data
```python
df = sns.load_dataset("tips")
df.head()
```
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>total_bill</th>
<th>tip</th>
<th>sex</th>
<th>smoker</th>
<th>day</th>
<th>time</th>
<th>size</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>16.99</td>
<td>1.01</td>
<td>Female</td>
<td>No</td>
<td>Sun</td>
<td>Dinner</td>
<td>2</td>
</tr>
<tr>
<th>1</th>
<td>10.34</td>
<td>1.66</td>
<td>Male</td>
<td>No</td>
<td>Sun</td>
<td>Dinner</td>
<td>3</td>
</tr>
<tr>
<th>2</th>
<td>21.01</td>
<td>3.50</td>
<td>Male</td>
<td>No</td>
<td>Sun</td>
<td>Dinner</td>
<td>3</td>
</tr>
<tr>
<th>3</th>
<td>23.68</td>
<td>3.31</td>
<td>Male</td>
<td>No</td>
<td>Sun</td>
<td>Dinner</td>
<td>2</td>
</tr>
<tr>
<th>4</th>
<td>24.59</td>
<td>3.61</td>
<td>Female</td>
<td>No</td>
<td>Sun</td>
<td>Dinner</td>
<td>4</td>
</tr>
</tbody>
</table>
</div>
# T-Test 1 sample
H0 : "le pourboire moyen est de 3.5 €"
```python
df["tip"].describe()
```
count 244.000000
mean 2.998279
std 1.383638
min 1.000000
25% 2.000000
50% 2.900000
75% 3.562500
max 10.000000
Name: tip, dtype: float64
```python
ttest_1samp(df["tip"], popmean=3.5)
```
TtestResult(statistic=-5.664152292840388, pvalue=4.1605377123077016e-08, df=243)
# En résumé
```python
print("H0 : \"le pourboire moyen est de 3.5 €")
print()
p_value = ttest_1samp(df["tip"], popmean=3.5).pvalue
alpha = 0.02
if p_value<alpha:
print("Nous avons suffisamment d'évidences pour rejeter H0")
else:
print("Nous n'avons pas suffisamment d'évidences pour rejeter H0")
```
H0 : "le pourboire moyen est de 3.5 €
Nous avons suffisamment d'évidences pour rejeter H0