Old school vs. New School Statistics
Do you remember when you learned statistics? For a hypothesis test, we had to pull tables, define degrees of freedom, set the p-value ... this is old school statistics. The last 5-10 years have changed dramatically. I know it well because I learned statistics the old way as well.
Today, we favor simulations. Let's take an example of a Chi-Squared-Test where we compare the distribution of two categorical variables. In this case, we compare college vs. family income (college = yes or no, and family income = low, medium, high).
On the left, I implemented the Chi-Squared-Test the old school way and on the right, the new school way:
You see that we needed quite a few more lines of code for simulating the Chi-Squared-Test the modern approach. I used the awesome R package infer for creating the permutation test and visualization.
If you're interested in learning more, Jake Vanderplas published a fantastic video on YouTube. For those who do Data Science in Python, they might know his popular book "Python Data Science Handbook." In other words, Jake is seriously good at Data Science.