Residents of the Southeastern United States are all too familiar with charts, known as spaghetti models, such as the one above.^[1] They combine a collection of weather data to predict the most likely path of a hurricane. Each colored line represents one possible path. The group of squiggly lines can begin to resemble strands of spaghetti, hence the name. In this section, we will investigate methods for making these types of predictions.

An example of a “spaghetti model,” which can be used to predict possible paths of a tropical storm.

Construct Probability Models

Suppose we roll a six-sided number cube. Rolling a number cube is an example of an experiment, or an activity with an observable result. The numbers on the cube are possible results, or outcomes, of this experiment. The set of all possible outcomes of an experiment is called the sample space of the experiment. The sample space for this experiment is [latex]\left\{1,2,3,4,5,6\right\}[/latex]. An event is any subset of a sample space.

The likelihood of an event is known as probability. The probability of an event [latex]p[/latex] is a number that always satisfies [latex]0\le p\le 1[/latex], where 0 indicates an impossible event and 1 indicates a certain event. A probability model is a mathematical description of an experiment listing all possible outcomes and their associated probabilities. For instance, if there is a 1% chance of winning a raffle and a 99% chance of losing the raffle, a probability model would look much like the table below.

The sum of the probabilities listed in a probability model must equal 1, or 100%.

Computing Probabilities of Equally Likely Outcomes

Let [latex]S[/latex] be a sample space for an experiment. When investigating probability, an event is any subset of [latex]S[/latex]. When the outcomes of an experiment are all equally likely, we can find the probability of an event by dividing the number of outcomes in the event by the total number of outcomes in [latex]S[/latex]. Suppose a number cube is rolled, and we are interested in finding the probability of the event “rolling a number less than or equal to 4.” There are 4 possible outcomes in the event and 6 possible outcomes in [latex]S[/latex], so the probability of the event is [latex]\frac{4}{6}=\frac{2}{3}[/latex].

Probability for Multiple Events

We are often interested in finding the probability that one of multiple events occurs. Suppose we are playing a card game, and we will win if the next card drawn is either a heart or a king. We would be interested in finding the probability of the next card being a heart or a king. The union of two events [latex]E\text{ and }F,\text{written }E\cup F[/latex], is the event that occurs if either or both events occur.

[latex]P\left(E\cup F\right)=P\left(E\right)+P\left(F\right)-P\left(E\cap F\right)[/latex]

Suppose the spinner below is spun. We want to find the probability of spinning orange or spinning a [latex]b[/latex].

There are a total of 6 sections, and 3 of them are orange. So the probability of spinning orange is [latex]\frac{3}{6}=\frac{1}{2}[/latex]. There are a total of 6 sections, and 2 of them have a [latex]b[/latex]. So the probability of spinning a [latex]b[/latex] is [latex]\frac{2}{6}=\frac{1}{3}[/latex]. If we added these two probabilities, we would be counting the sector that is both orange and a [latex]b[/latex] twice. To find the probability of spinning an orange or a [latex]b[/latex], we need to subtract the probability that the sector is both orange and has a [latex]b[/latex].

[latex]\dfrac{1}{2}+\dfrac{1}{3}-\dfrac{1}{6}=\dfrac{2}{3}[/latex]

The probability of spinning orange or a [latex]b[/latex] is [latex]\dfrac{2}{3}[/latex].

Computing the Probability of Mutually Exclusive Events

Suppose the spinner from earlier is spun again, but this time we are interested in the probability of spinning an orange or a [latex]d[/latex]. There are no sectors that are both orange and contain a [latex]d[/latex], so these two events have no outcomes in common. Events are said to be mutually exclusive events when they have no outcomes in common. Because there is no overlap, there is nothing to subtract, so the general formula is

[latex]P\left(E\cup F\right)=P\left(E\right)+P\left(F\right)[/latex]

Notice that with mutually exclusive events, the intersection of [latex]E[/latex] and [latex]F[/latex] is the empty set. The probability of spinning an orange is [latex]\frac{3}{6}=\frac{1}{2}[/latex] and the probability of spinning a [latex]d[/latex] is [latex]\frac{1}{6}[/latex]. We can find the probability of spinning an orange or a [latex]d[/latex] simply by adding the two probabilities.

[latex]\begin{align}P\left(E\cup F\right)&=P\left(E\right)+P\left(F\right) \\ &=\frac{1}{2}+\frac{1}{6} \\ &=\frac{2}{3} \end{align}[/latex]

The probability of spinning an orange or a [latex]d[/latex] is [latex]\dfrac{2}{3}[/latex].

Find the Probability That an Even Will Not Happen

We have discussed how to calculate the probability that an event will happen. Sometimes, we are interested in finding the probability that an event will not happen. The complement of an event [latex]E[/latex], denoted [latex]{E}^{\prime }[/latex], is the set of outcomes in the sample space that are not in [latex]E[/latex]. For example, suppose we are interested in the probability that a horse will lose a race. If event [latex]W[/latex] is the horse winning the race, then the complement of event [latex]W[/latex] is the horse losing the race.

To find the probability that the horse loses the race, we need to use the fact that the sum of all probabilities in a probability model must be 1.

[latex]P\left({E}^{\prime }\right)=1-P\left(E\right)[/latex]

The probability of the horse winning added to the probability of the horse losing must be equal to 1. Therefore, if the probability of the horse winning the race is [latex]\frac{1}{9}[/latex], the probability of the horse losing the race is simply

[latex]1-\dfrac{1}{9}=\dfrac{8}{9}[/latex]

Computing Probability Using Counting Theory

Many interesting probability problems involve counting principles, permutations, and combinations. In these problems, we will use permutations and combinations to find the number of elements in events and sample spaces. These problems can be complicated, but they can be made easier by breaking them down into smaller counting problems.

Assume, for example, that a store has 8 cellular phones and that 3 of those are defective. We might want to find the probability that a couple purchasing 2 phones receives 2 phones that are not defective. To solve this problem, we need to calculate all of the ways to select 2 phones that are not defective as well as all of the ways to select 2 phones. There are 5 phones that are not defective, so there are [latex]C\left(5,2\right)[/latex] ways to select 2 phones that are not defective. There are 8 phones, so there are [latex]C\left(8,2\right)[/latex] ways to select 2 phones. The probability of selecting 2 phones that are not defective is:

[latex]\begin{align}\frac{\text{ways to select 2 phones that are not defective}}{\text{ways to select 2 phones}}&=\frac{C\left(5,2\right)}{C\left(8,2\right)} \\[1mm] &=\frac{10}{28} \\[1mm] &=\frac{5}{14} \end{align}[/latex]

Key Equations

Key Concepts

• Probability is always a number between 0 and 1, where 0 means an event is impossible and 1 means an event is certain.
• The probabilities in a probability model must sum to 1.
• When the outcomes of an experiment are all equally likely, we can find the probability of an event by dividing the number of outcomes in the event by the total number of outcomes in the sample space for the experiment.
• To find the probability of the union of two events, we add the probabilities of the two events and subtract the probability that both events occur simultaneously.
• To find the probability of the union of two mutually exclusive events, we add the probabilities of each of the events.
• The probability of the complement of an event is the difference between 1 and the probability that the event occurs.
• In some probability problems, we need to use permutations and combinations to find the number of elements in events and sample spaces.

Glossary

complement of an event the set of outcomes in the sample space that are not in the event [latex]E[/latex]

event any subset of a sample space

experiment an activity with an observable result

mutually exclusive events events that have no outcomes in common

outcomes the possible results of an experiment

probability a number from 0 to 1 indicating the likelihood of an event

probability model a mathematical description of an experiment listing all possible outcomes and their associated probabilities

sample space the set of all possible outcomes of an experiment

union of two events the event that occurs if either or both events occur