The goal to this study is to understand better how the Fitt's Law works and find some limitation about the way data are collected or treated.

The implementation of the experimentation can be found [here](https://gricad-gitlab.univ-grenoble-alpes.fr/coutrixc/m2r_pointingxp), and some explanation about the Fitt's Law [here](https://en.wikipedia.org/wiki/Fitts%27s_law).

## Method

As this exercise is not about programming, I simply used Google Sheet to generate graphs.

Here is the method I used to treat data :

- First, I took the data from the resulting tables of the Fitt's Law experiment. I only took the Mean table.

- I did the mean of all the values I had, regrouping data on the ID. The goal is just to have a mean value for each ID, regardless the width and the distance (that are already used to calculate the ID).

- At the end, plot the graphs.

Then, I did several experimentation, changing the parameters to understand better how Fitt's Law works and what are the benefits and drawbacks of this method.

The "default" parameters we can vary on in the experiment are :

- The width of the target (in pixels)

- The distance between targets (in pixels)

- The number of trials (in unit)

I will then discover new parameter to vary on with the experiments I conducted.

## Experiments

### Test 1 : comparing my performance to Cecile Coutrix's

At first, I wanted to see what kind to differences we can have between two individuals, doing the same experimentation with the same parameters.

I selected the same "default" parameters as Cecile :

- Width : 1,2,4 px

- Distance : 16,32,64 px

- Number of trials : 6

Then, I plotted my results and Cecile's in the same graph.