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Computational Statistics


Tutorial materials found here:


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.” Audience

Attendees should have at least basic level Python. No statistical background is required. We will work with Jupyter notebooks that use Pandas, NumPy and SciPy, but no prior experience with these libraries is required.

Attendees will learn about resampling and related tools that use random simulation to perform statistical inference, including estimation and hypothesis testing.


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