6.8: Theoretical and Experimental Probability
So far in this text, you have solved problems dealing with definite situations. In the next several chapters, we will begin to look at a branch of mathematics that deals with possible situations. The study of probability involves applying formulas to determine the likelihood of an event occurring.
Almost all companies use some form of probability. Automotive companies want to determine the likelihood of their new vehicle being a big seller. Cereal manufacturers want to know the probability that their cereal will sell more than the competition. Pharmaceutical corporations need to know the likelihood of a new drug harming those who take it. Even politicians want to know the probability of receiving enough votes to win the election.
Probabilities can start with an experiment. As you learned in a previous chapter, an experiment is a controlled study. For example, suppose you want to know if the likelihood, or probability, of getting tails when flipping a coin is actually \begin{align*}\frac{1}{2}\end{align*}. By randomly grabbing a penny and making a tally chart of heads and tails, you are performing an experiment.
The set of all possible outcomes is called the sample space of the experiment.
The sample space for tossing a coin is \begin{align*}\left \{heads,\ tails\right \}\end{align*}.
Example 1: List the sample space for rolling a die.
Solution: A die is a six-sided figure with dots representing the numbers one through six. So the sample space is all the possible outcomes (i.e., what numbers could possibly be rolled).
\begin{align*}S=\left \{1,2,3,4,5,6,\right \}\end{align*}
Once you have determined the number of items in the sample space, you can compute the probability of a particular event.
Any one possible outcome of the experiment is called an event.
Theoretical probability is a ratio expressing the ways to be successful to the total events in an experiment. A shorter way to write this is:
\begin{align*}\text{Probability} \ (success) = \frac{number \ of \ ways \ to \ get \ success}{total \ number \ of \ possible \ outcomes}\end{align*}
Probabilities are expressed three ways:
- As a fraction
- As a percent
- As a decimal
Suppose you wanted to know the probability of landing a head or a tail when flipping a coin.
There would be two ways to get success and two possible outcomes.
\begin{align*}P(success)=\frac{2}{2}=1\end{align*}
This is a very important concept of probability.
The sum of individual event probabilities has a sum of 100%, or 1.
Example 2: Determine the theoretical probability of rolling a five on a die.
Solution: There are six events in the sample space. There is one way to roll a five.
\begin{align*}P(rolling \ a \ 5)=\frac{1}{6} \approx 16.67\%\end{align*}
Conducting an Experiment
Conducting an experiment for probability purposes is also called probability simulation. Suppose you wanted to conduct the coin experiment in the lesson opener. By grabbing a random coin, flipping it, and recording what lands up is a probability simulation. You can also simulate an experiment using a graphing calculator application.
Performing an Experiment Using the TI-84 Graphing Calculator
There is an application on the TI calculators called the coin toss. Among others (including the dice roll, spinners, and picking random numbers), the coin toss is an excellent application for when you what to find the probabilities for a coin tossed more than four times or more than one coin being tossed multiple times.
Let’s say you want to see one coin being tossed one time. Here is what the calculator will show and the key strokes to get to this toss.
Let’s say you want to see one coin being tossed ten times. Here is what the calculator will show and the key strokes to get to this sequence. Try it on your own.
We can actually see how many heads and tails occurred in the tossing of the 10 coins. If you click on the right arrow \begin{align*}(>)\end{align*} the frequency label will show you how many of the tosses came up heads.
Using this information, you can determine the experimental probability of tossing a coin and seeing a tail on its landing.
The experimental probability is the ratio of the proposed outcome to the number of experiment trials.
\begin{align*}P(success)= \frac{number \ of \ times \ the \ event \ occurred}{total \ number \ of \ trials \ of \ experiment}\end{align*}
Example 3: Compare the theoretical probability of flipping a tail to the experimental probability of flipping a tail on a coin.
Solution: There are two events in the sample space. There is one way to flip a tail.
\begin{align*}P(flipping \ a \ tail)=\frac{1}{2}\end{align*}
The coin toss simulation the calculator performed stated there were six tails out of ten tosses.
\begin{align*}P(flipping \ a \ tail)=\frac{6}{10}\end{align*}
The experimental probability (60%) in this case is greater than the theoretical probability (50%).
Finding Odds For and Against
Odds are similar to probability with the exception of the ratio's denominator.
The odds in favor of an event is the ratio of the number of successful events to the number of non-successful events.
\begin{align*}\text{Odds} \ (success) = \frac{number \ of \ ways \ to \ get \ success}{number \ of \ ways \ to \ not \ get \ success}\end{align*}
For example, suppose we were interested in the odds of rolling a 5 on a die.
What if we were interested in determining the odds against rolling a 5 on a die. There are five outcomes other than a “5” and one outcome of a “5.”
\begin{align*}Odds \ against \ rolling \ a \ 5= \frac{5}{1}\end{align*}
Example 4: Find the odds against rolling a number larger than 2 on a standard die.
Solution: There are four outcomes on a standard die larger than \begin{align*}2: \left \{3,4,5,6\right \}\end{align*}.
\begin{align*}Odds \ against \ rolling>2= \frac{2}{4}\end{align*}
Notice the “odds against” ratio is the reciprocal of the “odds in favor” ratio.
Practice Set
- Define experimental probability.
- How is experimental probability different from theoretical probability?
- Complete the table below, converting between probability values.
Fraction | Decimal | Percent |
---|---|---|
98% | ||
0.015 | ||
\begin{align*}\frac{1}{16}\end{align*} | ||
\begin{align*}\frac{2}{3}\end{align*} | ||
62% | ||
0.73 |
Use the “SPINNER” application in the Probability Simulator for the following questions. Set the spinner to five pieces.
- What is the sample space?
- Find the theoretical probability \begin{align*}P\end{align*}(spinng \begin{align*}a\end{align*} 4).
- Conduct an experiment by spinning the spinner 15 times and recording each number the spinner lands on.
- What is the experimental probability \begin{align*}P\end{align*}(spinng \begin{align*}a\end{align*} 4)?
- Give an event with a 0% probability.
In 9 – 18, use a standard 52-card deck to answer the questions.
- How many values are in the sample space? What could be an easy way to list all these values?
- Determine \begin{align*}P\end{align*}(King).
- What are the odds against drawing a face card?
- What are the odds in favor of drawing a six?
- Determine \begin{align*}P\end{align*}(Diamond).
- Determine \begin{align*}P\end{align*}(Nine of Clubs).
- Determine \begin{align*}P\end{align*}(King or 8 of Hearts).
- What are the odds against drawing a spade?
- What are the odds against drawing a red card?
- Give an event with 100% probability.
- Jorge says it has a 60% chance of raining tomorrow. Is this a strong likelihood? Explain your reasoning.
- What is the probability there will be a hurricane in your area tomorrow? Why did you choose this percentage?
Consider flipping two coins at the same time.
- Write the sample space.
- What is the probability of flipping one head and one tail?
- What is \begin{align*}P\end{align*}(both heads)?
- Give an event with 100% probability.
- Conduct the experiment 20 times. Find the experimental probability for flipping both heads. Is this different from the theoretical probability?
Mixed Review
- Graph the following inequality on a number line \begin{align*}-2 \le w<6\end{align*}.
- Graph the inequality on a coordinate plane \begin{align*}-2 \le w<6\end{align*}.
- Solve and graph the solutions using a number line: \begin{align*}|n-3|>12\end{align*}.
- Is \begin{align*}n=4.175\end{align*} a solution to \begin{align*}|n-3|>12\end{align*}?
- Graph the function \begin{align*}g(x)=\frac{7}{2} x-8\end{align*}.
- Explain the pattern: 24, 19, 14, 9,....
- Simplify \begin{align*}(-3)\left (\frac{(29)(2)-8}{-10}\right )\end{align*}.