In this video, explore basic probability examples and calculations.
- Probabilities can come in many forms, but all probabilities must conform to some simple rules. Let's take a look at two different types of probability, coin flips and weather. Coin flips are an example of even odds. Two possible outcomes, heads and tails, each at 50% probability. Weather has weighted odds. While there are many possible weather outcomes, let's consider only two outcomes, rain or no rain. Depending on where you live, probabilities are different, but they are rarely even odds. For example, based on historical data, the daily probability of rain in Los Angeles is 7%. The chance of no rain is 93%. These are definitely not even odds. No rain is significantly more likely. London's probability of rain is 29% and the probability of no rain is 71%. Different from Los Angeles, but again, not even. Examples of even odds scenarios would include coin flips, rolling dice, picking cards from a standard deck, and raffles. Weighted odds scenarios tend to be tied to real-world. Weather, science, medicine, sports, and business. Typically, weighted odds have multiple variables associated with them. On the other hand, even odds are fairly straightforward with one variable, for example, the amount of sides on an object, like a coin or a die. But here's something very important to remember. No matter what, the sum of all probabilities will equal 100%. This is referred to as the sample space. So to recap, the probability of all possible outcomes must sum to 100%. Sometimes the probability of every possible outcome is equally as likely, equal odds. Sometimes some outcomes are more likely than others, weighted odds. But no matter what, the probability of an outcome can never be less than 0%, nor can it be greater than 100%.
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.