In this video, get a brief introduction to multiple event probability, dependent versus independent events, and Bayes Theorem.
- Heads or tails, rain or no rain? These are relatively simple probabilities. We calculate the probability. We observe a single event. We get a single outcome. All of it, simple, but life is more complex. Every day we observe and consider multiple events. Consider one game of soccer. A team may win or lose, a player may score a goal, or they might not. Both events have their own individual probability, but is there a relationship between team wins and this player's success in scoring? Here's another example, consider these two statistics. First, one in 10,000 people gets a particular rare disease. Second, the test used to check for the disease is only accurate 98% of the time, but if a person gets a positive result indicating that they have the disease, the 98% accuracy of the test means that their tests could be wrong. How do we find the probability that a positive test was inaccurate? One more, in a class of 20 graduate students, only four will get a chance to interview with Microsoft. Mohan and Lily are friends. They both want to get an interview. What's the probability that both will get one of the four available slots? Suppose Mohan gets chosen for the first slot. Now what's the probability that Lily will get one of the three remaining slots? All three of these scenarios contain multiple events, and all three require a different probability logic, and in some cases, some new probability tools. In this chapter, we'll discuss multiple event probabilities and concepts like conditional probability, dependent versus independent events, and we'll look at probability trees and Bayes' theorem, which are both useful in managing multiple event scenarios.
Eddie explains that probability is used to make decisions about future outcomes and to understand past outcomes. He covers permutations, combinations, and percentiles, and goes into how to describe and calculate them. Eddie introduces multiple event probabilities and discusses when to add and subtract probabilities. He describes probability trees, Bayes’ Theorem, binomials, and so much more. You can learn to understand your data, prove theories, and save valuable resources—all by understanding the numbers.