Code for a workshop on statistical interference using computational methods in Python.

View the Project on GitHub AllenDowney/CompStats

Tutorial: Computational Statistics

Allen Downey

Do you know the difference between standard deviation and standard error? Do you know what statistical test to use for any occasion? Do you really know what a p-value is? How about a confidence interval?

Most people don’t really understand these concepts, even after taking several statistics classes. The problem is that these classes focus on mathematical methods that bury the concepts under a mountain of details.

This tutorial uses Python to implement simple statistical experiments that develop deep understanding. I will present examples using real-world data to answer relevant questions, and attendees will practice with hands-on exercises.

The tutorial material is based on my book, Think Stats, a class I teach at Olin College, and my blog, “Probably Overthinking It.”

Installation instructions

Note: Please try to install everything you need for this tutorial before you leave home!

To prepare for this tutorial, you have two options:

  1. Install Jupyter on your laptop and download my code from Git.

  2. Run the Jupyter notebook on a virtual machine on Binder.

I’ll provide instructions for both, but here’s the catch: if everyone chooses Option 2, the wireless network will fail and no one will be able to do the hands-on part of the workshop.

So, I strongly encourage you to try Option 1 and only resort to Option 2 if you can’t get Option 1 working.

Option 1A: If you already have Jupyter installed.

To do the exercises, you need Python 2 or 3 with NumPy, SciPy, and matplotlib. If you are not sure whether you have those modules already, the easiest way to check is to run my code and see if it works.

Code for this workshop is in a Git repository on Github.
If you have a Git client installed, you should be able to download it by running:

git clone

It should create a directory named CompStats. Otherwise you can download the repository in this zip file.

To start Jupyter, run:

cd CompStats
jupyter notebook

Jupyter should launch your default browser or open a tab in an existing browser window. If not, the Jupyter server should print a URL you can use. For example, when I launch Jupyter, I get

    ~/ThinkComplexity2$ jupyter notebook
    [I 10:03:20.115 NotebookApp] Serving notebooks from local directory: /home/downey/CompStats
    [I 10:03:20.115 NotebookApp] 0 active kernels
    [I 10:03:20.115 NotebookApp] The Jupyter Notebook is running at: http://localhost:8888/
    [I 10:03:20.115 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).

In this case, the URL is http://localhost:8888.
When you start your server, you might get a different URL. Whatever it is, if you paste it into a browser, you should should see a home page with a list of the notebooks in the repository.

Click on effect_size.ipynb. It should open the first notebook for the tutorial.

Select the cell with the import statements and press “Shift-Enter” to run the code in the cell. If it works and you get no error messages, you are all set.

If you get error messages about missing packages, you can install the packages you need using your package manager, or try Option 1B and install Anaconda.

Option 1B: If you don’t already have Jupyter.

I highly recommend installing Anaconda, which is a Python distribution that contains everything you need for this tutorial. It is easy to install on Windows, Mac, and Linux, and because it does a user-level install, it will not interfere with other Python installations.

Information about installing Anaconda is here.

When you install Anaconda, you should get Jupyter by default, but if not, run

conda install jupyter

Then go to Option 1A to make sure you can run my code.

If you don’t want to install Anaconda, you can see some other options here.

Option 2: only if Option 1 failed.

You can run my notebook in a virtual machine on Binder. To launch the VM, press this button:


You should see a home page with a list of the files in the repository.

If you want to try the exercises, open effect_size.ipynb. If you just want to see the answers, open effect_size_soln.ipynb. Either way, you should be able to run the notebooks in your browser and try out the examples.

However, be aware that the virtual machine you are running is temporary. If you leave it idle for more than an hour or so, it will disappear along with any work you have done.

Special thanks to the generous people who run Binder, which makes it easy to share and reproduce computation.