# From Groups to Individuals: Permutation Testing

Introduction to permutation testing. A typical situation regarding solving an experimental question using a data-driven approach involves several groups that differ in (hopefully) one, sometimes more variables.

## Approaching group data (between-group)

A typical situation regarding solving an experimental question using a data-driven approach involves several groups that differ in (hopefully) one, sometimes more variables.

Say you collect data on people that either ate (Group 1) or did not eat chocolate (Group 2). Because you know the literature very well, and you are an expert in your field, you believe that people that ate chocolate are more likely to ride camels than people that did not eat the chocolate.

You now want to prove that empirically.

I will be generating simulation data using Python, to demonstrate how permutation testing can be a great tool to detect within-group variations that could reveal peculiar patterns of some individuals. If your two groups are statistically different, then you might explore what underlying parameters could account for this difference. If your two groups are not different, you might want to explore whether some data points still behave “weirdly”, to decide whether to keep on collecting data or dropping the topic.

``````## Load standard libraries
import panda as pd
import numpy as np
import matplotlib.pyplot as plt``````

Now one typical approach in this (a bit crazy) experimental situation would be to look at the difference in camel riding propensity in each group. You could compute the proportions of camel riding actions, or the time spent on a camel, or any other dependent variable that might capture the effect you believe to be true.

### Generating data

Let’s generate the distribution of the chocolate group:

``````## Set seed for replicability
np.random.seed(42)

## Set Mean, SD and sample size
mean = 10; sd=1; sample_size=1000
## Generate distribution according to parameters
chocolate_distibution = np.random.normal(loc=mean, scale=sd, s
size=sample_size)
## Show data
plt.hist(chocolate_distibution)
plt.ylabel("Time spent on a camel")
plt.title("Chocolate Group")``````

Figure 1 | Histogram depicting the number of people that rode the camel in the chocolate group, per minute bin.

As you can see, I created a distribution centered around 10mn. Now let’s create the second distribution, which could be the control, centered at 9mn.

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