Chapter 2. Probability
When you think of probability, what images come to mind? Perhaps you think of gambling-related examples, like the probability of winning the lottery or getting a pair with two dice. Maybe it is predicting stock performance, the outcome of a political election, or whether your flight will arrive on time. Our world is full of uncertainties we want to measure.
Maybe that is the word we should focus on: uncertainty. How do we measure something that we are uncertain about?
In the end, probability is the theoretical study of measuring certainty that an event will happen. It is a foundational discipline for statistics, hypothesis testing, machine learning, and other topics in this book. A lot of folks take probability for granted and assume they understand it. However, it is more nuanced and complicated than most people think. While the theorems and ideas of probability are mathematically sound, it gets more complex when we introduce data and venture into statistics. We will cover that in Chapter 3 on statistics and hypothesis testing.
In this chapter, we will discuss what probability is. Then we will cover probability math concepts, Bayes’ Theorem, the binomial distribution, and the beta distribution.
Understanding Probability
Probability is how strongly we believe an event will happen, often expressed as a percentage. Here are some questions that might warrant a probability for an answer:
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How likely will I get 7 heads in 10 fair coin flips?
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What are my chances ...
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