Skip to ContentGo to accessibility pageKeyboard shortcuts menu
OpenStax Logo
Contemporary Mathematics

7.5 Basic Concepts of Probability

Contemporary Mathematics7.5 Basic Concepts of Probability

Menu
Table of contents
  1. Preface
  2. 1 Sets
    1. Introduction
    2. 1.1 Basic Set Concepts
    3. 1.2 Subsets
    4. 1.3 Understanding Venn Diagrams
    5. 1.4 Set Operations with Two Sets
    6. 1.5 Set Operations with Three Sets
    7. Chapter Summary
      1. Key Terms
      2. Key Concepts
      3. Videos
      4. Formula Review
      5. Projects
      6. Chapter Review
      7. Chapter Test
  3. 2 Logic
    1. Introduction
    2. 2.1 Statements and Quantifiers
    3. 2.2 Compound Statements
    4. 2.3 Constructing Truth Tables
    5. 2.4 Truth Tables for the Conditional and Biconditional
    6. 2.5 Equivalent Statements
    7. 2.6 De Morgan’s Laws
    8. 2.7 Logical Arguments
    9. Chapter Summary
      1. Key Terms
      2. Key Concepts
      3. Videos
      4. Projects
      5. Chapter Review
      6. Chapter Test
  4. 3 Real Number Systems and Number Theory
    1. Introduction
    2. 3.1 Prime and Composite Numbers
    3. 3.2 The Integers
    4. 3.3 Order of Operations
    5. 3.4 Rational Numbers
    6. 3.5 Irrational Numbers
    7. 3.6 Real Numbers
    8. 3.7 Clock Arithmetic
    9. 3.8 Exponents
    10. 3.9 Scientific Notation
    11. 3.10 Arithmetic Sequences
    12. 3.11 Geometric Sequences
    13. Chapter Summary
      1. Key Terms
      2. Key Concepts
      3. Videos
      4. Formula Review
      5. Projects
      6. Chapter Review
      7. Chapter Test
  5. 4 Number Representation and Calculation
    1. Introduction
    2. 4.1 Hindu-Arabic Positional System
    3. 4.2 Early Numeration Systems
    4. 4.3 Converting with Base Systems
    5. 4.4 Addition and Subtraction in Base Systems
    6. 4.5 Multiplication and Division in Base Systems
    7. Chapter Summary
      1. Key Terms
      2. Key Concepts
      3. Videos
      4. Projects
      5. Chapter Review
      6. Chapter Test
  6. 5 Algebra
    1. Introduction
    2. 5.1 Algebraic Expressions
    3. 5.2 Linear Equations in One Variable with Applications
    4. 5.3 Linear Inequalities in One Variable with Applications
    5. 5.4 Ratios and Proportions
    6. 5.5 Graphing Linear Equations and Inequalities
    7. 5.6 Quadratic Equations with Two Variables with Applications
    8. 5.7 Functions
    9. 5.8 Graphing Functions
    10. 5.9 Systems of Linear Equations in Two Variables
    11. 5.10 Systems of Linear Inequalities in Two Variables
    12. 5.11 Linear Programming
    13. Chapter Summary
      1. Key Terms
      2. Key Concepts
      3. Videos
      4. Formula Review
      5. Projects
      6. Chapter Review
      7. Chapter Test
  7. 6 Money Management
    1. Introduction
    2. 6.1 Understanding Percent
    3. 6.2 Discounts, Markups, and Sales Tax
    4. 6.3 Simple Interest
    5. 6.4 Compound Interest
    6. 6.5 Making a Personal Budget
    7. 6.6 Methods of Savings
    8. 6.7 Investments
    9. 6.8 The Basics of Loans
    10. 6.9 Understanding Student Loans
    11. 6.10 Credit Cards
    12. 6.11 Buying or Leasing a Car
    13. 6.12 Renting and Homeownership
    14. 6.13 Income Tax
    15. Chapter Summary
      1. Key Terms
      2. Key Concepts
      3. Videos
      4. Formula Review
      5. Projects
      6. Chapter Review
      7. Chapter Test
  8. 7 Probability
    1. Introduction
    2. 7.1 The Multiplication Rule for Counting
    3. 7.2 Permutations
    4. 7.3 Combinations
    5. 7.4 Tree Diagrams, Tables, and Outcomes
    6. 7.5 Basic Concepts of Probability
    7. 7.6 Probability with Permutations and Combinations
    8. 7.7 What Are the Odds?
    9. 7.8 The Addition Rule for Probability
    10. 7.9 Conditional Probability and the Multiplication Rule
    11. 7.10 The Binomial Distribution
    12. 7.11 Expected Value
    13. Chapter Summary
      1. Key Terms
      2. Key Concepts
      3. Formula Review
      4. Projects
      5. Chapter Review
      6. Chapter Test
  9. 8 Statistics
    1. Introduction
    2. 8.1 Gathering and Organizing Data
    3. 8.2 Visualizing Data
    4. 8.3 Mean, Median and Mode
    5. 8.4 Range and Standard Deviation
    6. 8.5 Percentiles
    7. 8.6 The Normal Distribution
    8. 8.7 Applications of the Normal Distribution
    9. 8.8 Scatter Plots, Correlation, and Regression Lines
    10. Chapter Summary
      1. Key Terms
      2. Key Concepts
      3. Videos
      4. Formula Review
      5. Projects
      6. Chapter Review
      7. Chapter Test
  10. 9 Metric Measurement
    1. Introduction
    2. 9.1 The Metric System
    3. 9.2 Measuring Area
    4. 9.3 Measuring Volume
    5. 9.4 Measuring Weight
    6. 9.5 Measuring Temperature
    7. Chapter Summary
      1. Key Terms
      2. Key Concepts
      3. Videos
      4. Formula Review
      5. Projects
      6. Chapter Review
      7. Chapter Test
  11. 10 Geometry
    1. Introduction
    2. 10.1 Points, Lines, and Planes
    3. 10.2 Angles
    4. 10.3 Triangles
    5. 10.4 Polygons, Perimeter, and Circumference
    6. 10.5 Tessellations
    7. 10.6 Area
    8. 10.7 Volume and Surface Area
    9. 10.8 Right Triangle Trigonometry
    10. Chapter Summary
      1. Key Terms
      2. Key Concepts
      3. Videos
      4. Formula Review
      5. Projects
      6. Chapter Review
      7. Chapter Test
  12. 11 Voting and Apportionment
    1. Introduction
    2. 11.1 Voting Methods
    3. 11.2 Fairness in Voting Methods
    4. 11.3 Standard Divisors, Standard Quotas, and the Apportionment Problem
    5. 11.4 Apportionment Methods
    6. 11.5 Fairness in Apportionment Methods
    7. Chapter Summary
      1. Key Terms
      2. Key Concepts
      3. Videos
      4. Formula Review
      5. Projects
      6. Chapter Review
      7. Chapter Test
  13. 12 Graph Theory
    1. Introduction
    2. 12.1 Graph Basics
    3. 12.2 Graph Structures
    4. 12.3 Comparing Graphs
    5. 12.4 Navigating Graphs
    6. 12.5 Euler Circuits
    7. 12.6 Euler Trails
    8. 12.7 Hamilton Cycles
    9. 12.8 Hamilton Paths
    10. 12.9 Traveling Salesperson Problem
    11. 12.10 Trees
    12. Chapter Summary
      1. Key Terms
      2. Key Concepts
      3. Videos
      4. Formula Review
      5. Projects
      6. Chapter Review
      7. Chapter Test
  14. 13 Math and...
    1. Introduction
    2. 13.1 Math and Art
    3. 13.2 Math and the Environment
    4. 13.3 Math and Medicine
    5. 13.4 Math and Music
    6. 13.5 Math and Sports
    7. Chapter Summary
      1. Key Terms
      2. Key Concepts
      3. Formula Review
      4. Projects
      5. Chapter Review
      6. Chapter Test
  15. A | Co-Req Appendix: Integer Powers of 10
  16. Answer Key
    1. Chapter 1
    2. Chapter 2
    3. Chapter 3
    4. Chapter 4
    5. Chapter 5
    6. Chapter 6
    7. Chapter 7
    8. Chapter 8
    9. Chapter 9
    10. Chapter 10
    11. Chapter 11
    12. Chapter 12
    13. Chapter 13
  17. Index
A close-up of a hand holding two dice.
Figure 7.26 When you roll two dice, some outcomes (like rolling a sum of seven) are more likely than others (rolling a sum of twelve). (credit: “Dice Isn’t Just A Game; It's A Way of Life” by Leah Love/Flickr, CC BY 2.0)

Learning Objectives

After completing this section, you should be able to:

  1. Define probability including impossible and certain events.
  2. Calculate basic theoretical probabilities.
  3. Calculate basic empirical probabilities.
  4. Distinguish among theoretical, empirical, and subjective probability.
  5. Calculate the probability of the complement of an event.

It all comes down to this. The game of Monopoly that started hours ago is in the home stretch. Your sister has the dice, and if she rolls a 4, 5, or 7 she’ll land on one of your best spaces and the game will be over. How likely is it that the game will end on the next turn? Is it more likely than not? How can we measure that likelihood? This section addresses this question by introducing a way to measure uncertainty.

Introducing Probability

Uncertainty is, almost by definition, a nebulous concept. In order to put enough constraints on it that we can mathematically study it, we will focus on uncertainty strictly in the context of experiments. Recall that experiments are processes whose outcomes are unknown; the sample space for the experiment is the collection of all those possible outcomes. When we want to talk about the likelihood of particular outcomes, we sometimes group outcomes together; for example, in the Monopoly example at the beginning of this section, we were interested in the roll of 2 dice that might fall as a 4, 5, or 7. A grouping of outcomes that we’re interested in is called an event. In other words, an event is a subset of the sample space of an experiment; it often consists of the outcomes of interest to the experimenter.

Once we have defined the event that interests us, we can try to assess the likelihood of that event. We do that by assigning a number to each event (EE) called the probability of that event (P(E)P(E)). The probability of an event is a number between 0 and 1 (inclusive). If the probability of an event is 0, then the event is impossible. On the other hand, an event with probability 1 is certain to occur. In general, the higher the probability of an event, the more likely it is that the event will occur.

Example 7.16

Determining Certain and Impossible Events

Consider an experiment that consists of rolling a single standard 6-sided die (with faces numbered 1-6). Decide if these probabilities are equal to zero, equal to one, or somewhere in between.

  1. P(roll a 4)P(roll a 4)
  2. P(roll a 7)P(roll a 7)
  3. P(roll a positive number)P(roll a positive number)
  4. P(roll a13)P(roll a13)
  5. P(roll an even number)P(roll an even number)
  6. P(roll a single-digit number)P(roll a single-digit number)

Your Turn 7.16

Jorge is about to conduct an experiment that consists of flipping a coin 4 times and recording the number of heads \text{(H)}. Decide if these probabilities are equal to zero, equal to one, or somewhere in between.
1.
P(\text{H} < 5)
2.
P(\text{H} < 4)
3.
P(\text{H} ≥ 5)

Three Ways to Assign Probabilities

The probabilities of events that are certain or impossible are easy to assign; they’re just 1 or 0, respectively. What do we do about those in-between cases, for events that might or might not occur? There are three methods to assign probabilities that we can choose from. We’ll discuss them here, in order of reliability.

Method 1: Theoretical Probability

The theoretical method gives the most reliable results, but it cannot always be used. If the sample space of an experiment consists of equally likely outcomes, then the theoretical probability of an event is defined to be the ratio of the number of outcomes in the event to the number of outcomes in the sample space.

FORMULA

For an experiment whose sample space SS consists of equally likely outcomes, the theoretical probability of the event EE is the ratio

P(E)=n(E)n(S),P(E)=n(E)n(S),

where n(E)n(E) and n(S)n(S) denote the number of outcomes in the event and in the sample space, respectively.

Example 7.17

Computing Theoretical Probabilities

Recall that a standard deck of cards consists of 52 unique cards which are labeled with a rank (the whole numbers from 2 to 10, plus J, Q, K, and A) and a suit (, , , or ). A standard deck is thoroughly shuffled, and you draw one card at random (so every card has an equal chance of being drawn). Find the theoretical probability of each of these events:

  1. The card is 1010.
  2. The card is a .
  3. The card is a king (K).

Your Turn 7.17

You are about to roll a fair (meaning that each face has an equal chance of landing up) 6-sided die, whose faces are labeled with the numbers 1 through 6. Find the theoretical probabilities of each outcome.
1.
You roll a 4.
2.
You roll a number greater than 2.
3.
You roll an odd number.

Checkpoint

It is critical that you make sure that every outcome in a sample space is equally likely before you compute theoretical probabilities!

Example 7.18

Using Tables to Find Theoretical Probabilities

In the Basic Concepts of Probability, we were considering a Monopoly game where, if your sister rolled a sum of 4, 5, or 7 with 2 standard dice, you would win the game. What is the probability of this event? Use tables to determine your answer.

Your Turn 7.18

1.
If you roll a pair of 4-sided dice with faces labeled with the numbers 1 through 4, what is the probability of rolling a sum of 6 or 7?

Example 7.19

Using Tree Diagrams to Compute Theoretical Probability

If you flip a fair coin 3 times, what is the probability of each event? Use a tree diagram to determine your answer

  1. You flip exactly 2 heads.
  2. You flip 2 consecutive heads at some point in the 3 flips.
  3. All 3 flips show the same result.

Your Turn 7.19

You have a modified deck of cards containing only 3, 4, and 5. You draw 2 two cards without replacing them (where order matters). What is the probability of each event?
1.
The first card drawn is 3.
2.
The first card drawn has a lower number than the second card.
3.
One of the cards drawn is 4.

People in Mathematics

Gerolamo Cardano

The first known text that provided a systematic approach to probabilities was written in 1564 by Gerolamo Cardano (1501–1576). Cardano was a physician whose illegitimate birth closed many doors that would have otherwise been open to someone with a medical degree in 16th-century Italy. As a result, Cardano often turned to gambling to help ends meet. He was a remarkable mathematician, and he used his knowledge to gain an edge when playing at cards or dice. His 1564 work, titled Liber de ludo aleae (which translates as Book on Games of Chance), summarized everything he knew about probability. Of course, if that book fell into the hands of those he played against, his advantage would disappear. That’s why he never allowed it to be published in his lifetime (it was eventually published in 1663). Cardano made other contributions to mathematics; he was the first person to publish the third degree analogue of the Quadratic Formula (though he didn’t discover it himself), and he popularized the use of negative numbers.

Method 2: Empirical Probability

Theoretical probabilities are precise, but they can’t be found in every situation. If the outcomes in the sample space are not equally likely, then we’re out of luck. Suppose you’re watching a baseball game, and your favorite player is about to step up to the plate. What is the probability that he will get a hit?

In this case, the sample space is {hit, not a hit}. That doesn’t mean that the probability of a hit is 1212, since those outcomes aren’t equally likely. The theoretical method simply can’t be used in this situation. Instead, we might look at the player’s statistics up to this point in the season, and see that he has 122 hits in 531 opportunities. So, we might think that the probability of a hit in the next plate appearance would be about 1225310.231225310.23. When we use the outcomes of previous replications of an experiment to assign a probability to the next replication, we’re defining an empirical probability. Empirical probability is assigned using the outcomes of previous replications of an experiment by finding the ratio of the number of times in the previous replications the event occurred to the total number of previous replications.

Empirical probabilities aren’t exact, but when the number of previous replications is large, we expect them to be close. Also, if the previous runs of the experiment are not conducted under the exact set of circumstances as the one we’re interested in, the empirical probability is less reliable. For instance, in the case of our favorite baseball player, we might try to get a better estimate of the probability of a hit by looking only at his history against left- or right-handed pitchers (depending on the handedness of the pitcher he’s about to face).

Who Knew?

Probability and Statistics

One of the broad uses of statistics is called statistical inference, where statisticians use collected data to make a guess (or inference) about the population the data were collected from. Nearly every tool that statisticians use for inference is based on probability. Not only is the method we just described for finding empirical probabilities one type of statistical inference, but some more advanced techniques in the field will give us an idea of how close that empirical probability might be to the actual probability!

Example 7.20

Finding Empirical Probabilities

Assign an empirical probability to the following events:

  1. Jose is on the basketball court practicing his shots from the free throw line. He made 47 out of his last 80 attempts. What is the probability he makes his next shot?
  2. Amy is about to begin her morning commute. Over her last 60 commutes, she arrived at work 12 times in under half an hour. What is the probability that she arrives at work in 30 minutes or less?
  3. Felix is playing Yahtzee with his sister. Felix won 14 of the last 20 games he played against her. How likely is he to win this game?

Your Turn 7.20

1.
Jessie is in charge of quality control at a factory manufacturing SUVs. Today, she’s checking the placement of the taillight housing. Of the last thousand units off the line, 13 had faulty placement. What empirical probability might Jesse assign to the next vehicle coming off the line having bad placement?

WORK IT OUT

Buffon’s Needle

A famous early question about probability (posed by Georges-Louis Leclerc, Comte de Buffon in the 18th century) had to do with the probability that a needle dropped on a floor finished with wooden slats would lay across one of the seams. If the distance between the slats is exactly the same length as the needle, then it can be shown using calculus that the probability that the needle crosses a seam is 2π2π. Using toothpicks or matchsticks (or other uniformly long and narrow objects), assign an empirical probability to this experiment by drawing parallel lines on a large sheet of paper where the distance between the lines is equal to the length of your dropping object, then repeatedly dropping the objects and noting whether the object touches one of the lines. Once you have your empirical probability, take its reciprocal and multiply by 2. Is the result close to ππ?

Method 3: Subjective Probability

In cases where theoretical probability can’t be used and we don’t have prior experience to inform an empirical probability, we’re left with one option: using our instincts to guess at a subjective probability. A subjective probability is an assignment of a probability to an event using only one’s instincts.

Subjective probabilities are used in cases where an experiment can only be run once, or it hasn’t been run before. Because subjective probabilities may vary widely from person to person and they’re not based on any mathematical theory, we won’t give any examples. However, it’s important that we be able to identify a subjective probability when we see it; they will in general be far less accurate than empirical or theoretical probabilities.

Example 7.21

Distinguishing among Theoretical, Empirical, and Subjective Probabilities

Classify each of the following probabilities as theoretical, empirical, or subjective.

  1. An eccentric billionaire is testing a brand new rocket system. He says there is a 15% chance of failure.
  2. With 4 seconds to go in a close basketball playoff game, the home team need 3 points to tie up the game and send it to overtime. A TV commentator says that team captain should take the final 3-point shot, because he has a 38% chance of making it (greater than every other player on the team).
  3. Felix is losing his Yahtzee game against his sister. He has one more chance to roll 2 dice; he’ll win the game if they both come up 4. The probability of this is about 2.8%.

Your Turn 7.21

Classify each of the following probabilities as theoretical, empirical, or subjective.
1.
You have entered a raffle with 500 entrants. Your probability of winning is 0.2%.
2.
Your little brother takes the bus to school each morning. On the first day of school, you believe that the probability that the bus arrives between 7:15 AM and 7:30 AM is about 80%.
3.
Your little brother takes the bus to school each morning. On the last day of school, you believe that the probability that the bus arrives between 7:15 AM and 7:30 AM is about 73%.

Who Knew?

Benford’s Law

In 1938, Frank Benford published a paper (“The law of anomalous numbers,” in Proceedings of the American Philosophical Society) with a surprising result about probabilities. If you have a list of numbers that spans at least a couple of orders of magnitude (meaning that if you divide the largest by the smallest, the result is at least 100), then the digits 1–9 are not equally likely to appear as the first digit of those numbers, as you might expect. Benford arrived at this conclusion using empirical probabilities; he found that 1 was about 6 times as likely to be the initial digit as 9 was!

New Probabilities from Old: Complements

One of the goals of the rest of this chapter is learning how to break down complicated probability calculations into easier probability calculations. We’ll look at the first of the tools we can use to accomplish this goal in this section; the rest will come later.

Given an event EE, the complement of EE (denoted EE) is the collection of all of the outcomes that are not in EE. (This is language that is taken from set theory, which you can learn more about elsewhere in this text.) Since every outcome in the sample space either is or is not in EE, it follows that n(E)+n(E)=n(S)n(E)+n(E)=n(S). So, if the outcomes in SS are equally likely, we can compute theoretical probabilities P(E)=n(E)n(S)P(E)=n(E)n(S) and P(E)=n(E)n(S)P(E)=n(E)n(S). Then, adding these last two equations, we get

P(E)+P(E)=n(E)n(S)+n(E)n(S)=n(E)+n(E)n(S)=n(S)n(S)=1P(E)+P(E)=n(E)n(S)+n(E)n(S)=n(E)+n(E)n(S)=n(S)n(S)=1

Thus, if we subtract P(E)P(E) from both sides, we can conclude that P(E)=1P(E)P(E)=1P(E). Though we performed this calculation under the assumption that the outcomes in SS are all equally likely, the last equation is true in every situation.

FORMULA

P(E)=1P(E)P(E)=1P(E)

How is this helpful? Sometimes it is easier to compute the probability that an event won’t happen than it is to compute the probability that it will. To apply this principle, it’s helpful to review some tricks for dealing with inequalities. If an event is defined in terms of an inequality, the complement will be defined in terms of the opposite inequality: Both the direction and the inclusivity will be reversed, as shown in the table below.

If EE is defined with: then EE is defined with:
<<
>>
>>
<<

Example 7.22

Using the Formula for Complements to Compute Probabilities

  1. If you roll a standard 6-sided die, what is the probability that the result will be a number greater than one?
  2. If you roll two standard 6-sided dice, what is the probability that the sum will be 10 or less?
  3. If you flip a fair coin 3 times, what is the probability that at least one flip will come up tails?

Your Turn 7.22

1.
If you roll a pair of 4-sided dice with faces labeled 1 through 4, what is the probability that the sum of the resulting numbers will be greater than 3? Hint: You found this sample space in an earlier Your Turn.

Check Your Understanding

You have two coins: a nickel and a quarter. You flip them both. Find the probabilities of these events:
22.
Both come up heads.
23.
The quarter comes up heads.
24.
You get one heads and one tails.
25.
You get three tails.
Decide whether the given probabilities were most likely derived theoretically, empirically, or subjectively.
26.
A poker player has a 16% chance of making a hand called a flush on the next card.
27.
Your friend Jacob tells you that there’s a 20% chance he’ll get married in the next 5 years.
28.
Ashley has a coin that they think might not be fair, so they flip it 100 times and note that the result was heads 58 times. So, Ashley says the probability of flipping heads on that coin is about 58%.
If you flip a fair coin 50 times, the probability of getting 20 or fewer heads is about 10.1% (a fact we’ll learn how to verify later).
29.
If E is the event “number of heads is 20 or fewer”, describe the event E^{\prime} using an inequality.
30.
Find P(E^{\prime} ).

Section 7.5 Exercises

For the following exercises, we are considering two special 6-sided dice. Each face is labeled with a number and a letter: the first die has faces 1A, 1B, 2A, 2C, 4A, 4E; the second has faces 1A, 1A, 2A, 2B, 3A, 3C. Assume that each face has an equal probability of landing face up.
1.
Use a table to identify the sample space of the experiment in which we roll both dice and note the sum of the two numbers that are showing.
2.
What is the probability that we roll a sum less than 8?
3.
What is the probability that we roll a sum larger than 8?
4.
What is the probability that we roll a sum less than or equal to 2?
5.
What is the probability that we roll a sum greater than 2?
6.
What is the probability that we roll an even sum?
7.
What is the probability that we roll an odd sum?
8.
Use a table to identify the sample space of the experiment in which we roll both dice and note the two letters that are showing.
9.
What is the probability that no As are showing?
10.
What is the probability that at least one A is showing?
11.
What is the probability that two As are showing?
12.
What is the probability that two of the same letter are showing?
13.
What is the probability that the letter on the first die comes alphabetically before the letter on the second die?
14.
What is the probability that two vowels are showing?
15.
What is the probability that two consonants are showing?
16.
What is the probability that one consonant and one vowel are showing?
In the following Exercises, decide whether the given probability was likely determined theoretically, empirically, or subjectively.
17.
Carolyn is breeding two pea plants, and notes that 8 of the 25 offspring plants have yellow peas. So, she concludes that there’s a 32% chance that an offspring of these two plants will have yellow peas.
18.
At the beginning of the semester, Malik estimates there’s a 30% chance that he’ll earn As in all his classes.
19.
Abbie is deciding where she will attend college in the fall. Right now, she thinks there’s an 80% chance she’ll attend an in-state school.
20.
If you roll three standard 6-sided dice, the probability that the sum will be 8 is \frac{{21}}{{216}}.
21.
According to the app he uses to play the game, Jason has won 18 of the last 100 games of solitaire he’s played. So, the probability that he wins the next one is about 18%.
22.
Jim and Anne are both in a club with 10 members. If 3 people are chosen at random to form a committee, then the probability that both Jim and Anne are chosen is \frac{1}{{15}}.

For the following exercises, use the following table of the top 15 players by number of plate appearances (PA) in the 2019 Major League Baseball season to assign empirical probabilities to the given events. A plate appearance is a batter’s opportunity to try to get a hit. The other columns are runs scored (R), hits (H), doubles (2B), triples (3B), home runs (HR), walks (BB), and strike outs (SO).

Name Team PA R H 2B 3B HR BB SO
Marcus Semien OAK 747 123 187 43 7 33 87 102
Whit Merrifield KCR 735 105 206 41 10 16 45 126
Ronald Acuna Jr. ATL 715 127 175 22 2 41 76 188
Jonathan Villar BAL 714 111 176 33 5 24 61 176
Mookie Betts BOS 706 135 176 40 5 29 97 101
Rhys Hoskins PHI 705 86 129 33 5 29 116 173
Jorge Polanco MIN 704 107 186 40 7 22 60 116
Rafael Devers BOS 702 129 201 54 4 32 48 119
Ozzie Albies ATL 702 102 189 43 8 24 54 112
Eduardo Escobar ARI 699 94 171 29 10 35 50 130
Xander Bogaerts BOS 698 110 190 52 0 33 76 122
José Abreu CHW 693 85 180 38 1 33 36 152
Pete Alonso NYM 693 103 155 30 2 53 72 183
Freddie Freeman ATL 692 113 176 34 2 38 87 127
Alex Bregman HOU 690 122 164 37 2 41 119 83
23.
Mookie Betts gets a home run in his next plate appearance.
24.
Xander Bogaerts strikes out in his next plate appearance.
25.
Jonathan Villar gets a hit in his next plate appearance.
26.
Rhys Hoskins gets a walk in his next plate appearance.
27.
José Abreu scores a run in his next plate appearance.
28.
Eduardo Escobar hits a triple in his next plate appearance.
29.
Whit Merrifield hits a double in his next plate appearance.
30.
Ronald Acuna Jr. gets an extra-base hit (double, triple, or home run) in his next plate appearance.
Citation/Attribution

Want to cite, share, or modify this book? This book uses the Creative Commons Attribution License and you must attribute OpenStax.

Attribution information
  • If you are redistributing all or part of this book in a print format, then you must include on every physical page the following attribution:
    Access for free at https://openstax.org/books/contemporary-mathematics/pages/1-introduction
  • If you are redistributing all or part of this book in a digital format, then you must include on every digital page view the following attribution:
    Access for free at https://openstax.org/books/contemporary-mathematics/pages/1-introduction
Citation information

© Apr 17, 2023 OpenStax. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution License . The OpenStax name, OpenStax logo, OpenStax book covers, OpenStax CNX name, and OpenStax CNX logo are not subject to the Creative Commons license and may not be reproduced without the prior and express written consent of Rice University.