This GitHub repo contains a Jupyter Notebook that explores some interesting topics in probability and statistics.

The notebook compares the binomial and hypergeometric distributions, which are both discrete probability distributions that model the number of successes in a fixed number of trials.

The notebook also analyses Zipf's law, which is an empirical law that states that the frequency of a word in a text is inversely proportional to its rank. The notebook uses a sample text from a book to demonstrate Zipf's law and plot the frequency-rank distribution.

Furthermore, the notebook analyses Benford's law, which is another empirical law that states that the leading digits of many real-life numbers follow a certain distribution. The notebook uses SciPy constants and random networks to generate numbers that follow Benford's law and plot the digit distribution.

Finally, the notebook finds the best fits of Weibull, log-normal, and Pareto distributions to Benford's distribution.