Probability Learning Objective(s) · Define event, outcome, trial, simple event, sample space and calculate the probability that an event will occur. · Calculate the probability of events for more complex outcomes. · Solve applications involving probabilities. Probability provides a measure of how likely it is that something will occur. It is a number between and including the numbers 0 and 1. It can be written as a fraction, a decimal, or a percent.
Picking numbers randomly means that there is no specific order in which they are chosen. Many games use dice or spinners to generate numbers randomly. If you understand how to calculate probabilities, you can make thoughtful decisions about how to play these games by knowing the likelihood of various outcomes. First you need to know some terms related to probability. When working with probability, a random action or series of actions is called a trial. An outcome is the result of a trial, and an event is a particular collection of outcomes. Events are usually described using a common characteristic of the outcomes. Let's apply this language to see how the terms work in practice. Some games require rolling a die with six sides, numbered from 1 to 6. (Dice is the plural of die.) The chart below illustrates the use of trial, outcome, and event for such a game:
Notice that a collection of outcomes is put in braces and separated by commas. A simple event is an event with only one outcome. Rolling a 1 would be a simple event, because there is only one outcome that works—1! Rolling more than a 5 would also be a simple event, because the event includes only 6 as a valid outcome. A compound event is an event with more than one outcome. For example, in rolling one six-sided die, rolling an even number could occur with one of three outcomes: 2, 4, and 6. When you roll a six-sided die many times, you should not expect any outcome to happen more often than another (assuming that it is a fair die). The outcomes in a situation like this are said to be equally likely. It’s very important to recognize when outcomes are equally likely when calculating probability. Since each outcome in the die-rolling trial is equally likely, you would expect to get each outcome of the rolls. That is, you'd expect of the rolls to be 1, of the rolls to be 2, of the rolls to be 3, and so on.
The probability of an event is how often it is expected to occur. It is the ratio of the size of the event space to the size of the sample space. First, you need to determine the size of the sample space. The size of the sample space is the total number of possible outcomes. For example, when you roll 1 die, the sample space is 1, 2, 3, 4, 5, or 6. So the size of the sample space is 6. Then you need to determine the size of the event space. The event space is the number of outcomes in the event you are interested in. The event space for rolling a number less than three is 1 or 2. So the size of the event space is 2. For equally likely outcomes, the probability of an event E can be written P(E).
It is a common practice with probabilities, as with fractions in general, to simplify a probability into lowest terms since that makes it easier for most people to get a sense of how great it is. Unless there is reason not to do so, express all final probabilities in lowest terms.
Counting Methods to find Sample Spaces The most difficult thing for calculating a probability can be finding the size of the sample space, especially if there are two or more trials. There are several counting methods that can help. The first one to look at is making a chart. In the example below, Tori is flipping two coins. So you need to determine the sample space carefully. A chart such as the one shown in the example that follows is a good approach.
In the example below, the sample space for Tori is simple as only one die is being rolled. However, since James is rolling two die, a chart helps to organize the information.
You can also use a tree diagram to determine the sample space. A tree diagram has a branch for every possible outcome for each event. Suppose a closet has three pairs of pants (black, white, and green), four shirts (green, white, purple, and yellow), and two pairs of shoes (black and white). How many different outfits can be made? There are 3 choices for pants, 4 choices for shirts, and 2 choices for shoes. For our tree diagram, let's use B for black, W for white, G for green, P for purple, and Y for yellow.
You can see from the tree diagram that there are 24 possible outfits (some perhaps not great choices) in the sample space. Now you could fairly easily solve some probability problems. For example, what is the probability that if you close your eyes and choose randomly you would choose pants and shoes with the same color? You can see that there are 8 outfits where the pants and the shoes match.
As you've seen, when a trial involves more than one random element, such as flipping more than one coin or rolling more than one die, you don't always need to identify every outcome in the sample space to calculate a probability. You only need the number of outcomes. The Fundamental Counting Principle is a way to find the number of outcomes without listing and counting every one of them.
So you could use the Fundamental Counting Principle to find out how many outfits there are in the previous example. There are 3 choices for pants, 4 choices for shirts, and 2 choices for shoes. Using The Fundamental Counting Principle, you have 4 • 3 • 2 = 24 different outfits.
Probability helps you understand random, unpredictable situations where multiple outcomes are possible. It is a measure of the likelihood of an event, and it depends on the ratio of event and possible outcomes, if all those outcomes are equally likely.
The Fundamental Counting Principle is a shortcut to finding the size of the sample space when there are many trials and outcomes: If one event has p possible outcomes, and another event has m possible outcomes, then there are a total of p • m possible outcomes for the two events. |