# T-Test in Python/v3

Learn how to perform a one sample and two sample t-test using Python.

**Note:**this page is part of the documentation for version 3 of Plotly.py, which is

*not the most recent version*.

See our Version 4 Migration Guide for information about how to upgrade.

#### New to Plotly?¶

Plotly's Python library is free and open source! Get started by dowloading the client and reading the primer.

You can set up Plotly to work in online or offline mode, or in jupyter notebooks.

We also have a quick-reference cheatsheet (new!) to help you get started!

```
import plotly.plotly as py
import plotly.graph_objs as go
from plotly.tools import FigureFactory as FF
import numpy as np
import pandas as pd
import scipy
```

#### Generate Data¶

Let us generate some random data from the `Normal Distriubtion`

. We will sample 50 points from a normal distribution with mean $\mu = 0$ and variance $\sigma^2 = 1$ and from another with mean $\mu = 2$ and variance $\sigma^2 = 1$.

```
data1 = np.random.normal(0, 1, size=50)
data2 = np.random.normal(2, 1, size=50)
```

The two normal probability distribution functions (p.d.f) stacked on top of each other look like this:

```
x = np.linspace(-4, 4, 160)
y1 = scipy.stats.norm.pdf(x)
y2 = scipy.stats.norm.pdf(x, loc=2)
trace1 = go.Scatter(
x = x,
y = y1,
mode = 'lines+markers',
name='Mean of 0'
)
trace2 = go.Scatter(
x = x,
y = y2,
mode = 'lines+markers',
name='Mean of 2'
)
data = [trace1, trace2]
py.iplot(data, filename='normal-dists-plot')
```

#### One Sample T Test¶

A `One Sample T-Test`

is a statistical test used to evaluate the null hypothesis that the mean $m$ of a 1D sample dataset of independant observations is equal to the true mean $\mu$ of the population from which the data is sampled. In other words, our null hypothesis is that

For our T-test, we will be using a significance level of `0.05`

. On the matter of doing ethical science, it is good practice to always state the chosen significance level for a given test *before* actually conducting the test. This is meant to ensure that the analyst does not modify the significance level for the purpose of achieving a desired outcome.

For more information on the choice of 0.05 for a significance level, check out this page.

```
true_mu = 0
onesample_results = scipy.stats.ttest_1samp(data1, true_mu)
matrix_onesample = [
['', 'Test Statistic', 'p-value'],
['Sample Data', onesample_results[0], onesample_results[1]]
]
onesample_table = FF.create_table(matrix_onesample, index=True)
py.iplot(onesample_table, filename='onesample-table')
```

Since our p-value is greater than our Test-Statistic, we have good evidence to not reject the null-hypothesis at the $0.05$ significance level. This is our expected result because the data was collected from a normal distribution.

#### Two Sample T Test¶

If we have two independently sampled datasets (with equal variance) and are interested in exploring the question of whether the true means $\mu_1$ and $\mu_2$ are identical, that is, if the data were sampled from the same population, we would use a `Two Sample T-Test`

.

Typically when a researcher in a field is interested in the affect of a given test variable between two populations, they will take one sample from each population and will note them as the experimental group and the control group. The experimental group is the sample which will receive the variable being tested, while the control group will not.

This test variable is observed (eg. blood pressure) for all the subjects and a two sided t-test can be used to investigate if the two groups of subjects were sampled from populations with the same true mean, i.e. "Does the drug have an effect?"

```
twosample_results = scipy.stats.ttest_ind(data1, data2)
matrix_twosample = [
['', 'Test Statistic', 'p-value'],
['Sample Data', twosample_results[0], twosample_results[1]]
]
twosample_table = FF.create_table(matrix_twosample, index=True)
py.iplot(twosample_table, filename='twosample-table')
```

Since our p-value is much less than our Test Statistic, then with great evidence we can reject our null hypothesis of identical means. This is in alignment with our setup, since we sampled from two different normal pdfs with different means.