
During this class you’ll learn what probability tells you, how to avoid the fallacies that mislead people, how to calculate probability by breaking the event down, what conditional probability tells you, how to use Bayes’ Rule to incorporate your knowledge and how to use expected value for your best guess on the average outcome. No math is required beyond basic arithmetic. Spreadsheets are used to demonstrate randomness and formulas, but their use is optional.
Probability is both a science and a precise way to talk about likelihood. You’ll learn to convert odds to probability and calculate expected value. You’ll learn to break a problem up into equally likely outcomes to easily compute probabilities. You’ll watch some simulated coin flips and random draws to develop intuition about randomness.
Many of the real-world problems you confront are too complex for count and divide. In fact, subjectivity often enters into probability, so that people may legitimately assess different probability values for the same event based on their knowledge. We broaden our definition of probability to help you solve the problems you need to solve.
Don’t get sucked in by these common fallacies that lead to bad decision-making. Probability is deceptively simple, so beware.
One of the most common fallacies is seeing randomly produced patterns and thinking they mean something, leading to spurious conclusions. The other is confusion of the inverse, in which conditional probabilities get turned around, giving spurious implications, leading to often silly conclusions.
Conditional probability is useful when you’re interested in events happening one after another, but it’s broader than that. It also helps you understand the confusion of the inverse and the concepts of false negative and false positive.
If you understand this, you’re ahead of most people, including many doctors. A prediction can fail in two different ways, a false positive or a false negative. We look at the implication of these values if you get a positive test for a rare condition. Beware the confusion of the inverse.
We look at an example for COVID tests. We summarize the four possible true and false outcomes of a test.
You can apply the count-and-divide technique to historical data you’ve collected. We look at an example of a bank finding a group more likely to default on their loans.
The real-world events you’re faced with usually have complex probabilities depending on multiple things happening. Learn how to combine simple probabilities to get the answers.
Try out these techniques in some exercises with solutions.
Use the science of probability to turn vague terms such as "likely" into precise values you can use to assess risks and alternatives. This beginning course gives you all you need to apply probability to real-world questions. Use Bayes' Rule to incorporate what you know about the outside world. Combine simple probabilities to find the likelihood of complex events. Use conditional probability to focus on groups or situations. Draw correct conclusions from conditional probabilities, including false positives and false negatives. Avoid the many probability fallacies that often lead to bad decisions. Try out your knowledge in exercises, then take on some tricky challenges (you'll get the solutions). A formula “cheat sheet” and optional spreadsheets are included.
What You'll Learn
Probability is both a science and a measure of likelihood. We go beyond the standard examples of cards and dice and discuss what it means for weather prediction or medical tests. Probability is useful for giving you an expected value, what you would expect the outcome to be on average. Yet, as we watch random events play out in a simulation, we see that we need to also expect the unexpected.
Probability appears simple and straightforward, which leads people to get misled by various common probability fallacies. One key fallacy is seeing patterns that aren't there. You will see examples in which ordinary randomness leads to silly conclusions. Another is the confusion of the inverse of conditional probabilities. This causes confusion in assessing the results of medical tests and making faulty causal connections.
From Your Instructor, Carol Jacoby
I’ve been using various types of analysis to answer tricky questions for over 30 years. I did this as a mission analyst at Hughes Electronics and other companies to predict outcomes and compare alternatives. The applications were broad and ill-defined: protect Europe from missile attack, limit drug smuggling, design a highway system for self-driving cars and more.
I have a PhD in mathematics, and I’ve been teaching technical classes to managers through major universities for 20 years. The students praise my enthusiasm and ability to make complex subjects clear. A common comment is, "I wish you had been my math teacher in high school." Here are samples of classes that were heavy in analysis.
· Predictive Analytics: Caltech Center for Technology and Management Education
· Lean Six Sigma: Caltech Center for Technology and Management Education
· Systems Engineering: UCLA Extension for Raytheon
· The Decisive Manager: UCLA Technical Management Program
One thing I like about teaching is interacting with the students. I look forward to comments and direct messages and respond promptly. Any feedback is encouraged. If something is confusing or doesn’t work as expected, I want to hear about it right away so I can fix it. I especially want to hear about your own data explorations and other topics you’d like to learn about or problems you’d like to solve.
So, are you ready to dig into that data and see what you can learn? Learn probability in just a couple hours. Sign up now. If you want more of a taste first, check out the quick promo video or some of the free lessons. I hope to see you in class.