3 So you’re telling me there’s a chance!
This chapter covers
- The fundamentals of probability and how to reason about uncertainty
- How probability distributions work and why they are useful
- Different probability distributions and their use cases
Probability is one of my favorite topics because its applications are endless: engineering, data science, machine learning, hypothesis testing, finance, biology, social sciences, just to name a few. I started my career writing deterministic algorithms and treating problems as if there was only one right answer. But as I got more ambitious in solving more difficult problems, I quickly realized how I could never eliminate uncertainty, and there could be many answers to a problem, with varying degrees of rightness. And that is really what probability is about: measuring uncertainty. I’m excited about this chapter because once we learn probability distributions, we can really start solving real-world problems. When we use probability distributions, we are entertaining all possible outcomes and how certain we are in each one. We err on the side of approximation rather than being adamant, and there is power in hedging our uncertainty. It makes us more objective. It makes our responses more calculated than reactive. As any seasoned investor will say, you do not have to be right all the time to get an edge. You just have to be right often enough.