Name: AP Statistics Course Book: The Power of Data
Author: VerityPrep

VerityPrep School Support

Prepare for AP Statistics with chapters on sampling, probability, confidence intervals, t tests and chi-square tests, plus project labs and AP-style FRQs.

School Support
AP
AP Statistics

Open the book Practice exams Download PDF

About this book

This course book is written for students taking AP Statistics and is built around one idea: turning data into evidence. It opens with a chapter on why statistics matters, how curiosity becomes evidence and how projects show your thinking, followed by an exam playbook, a formula field guide and a calculator field guide.

The chapters cover exploring one-variable data, two categorical variables, correlation and least-squares regression, then collecting data through random sampling and experimental design. Probability chapters cover simulation, conditional probability and independence, followed by random variables with the binomial and normal distributions, and sampling distributions with the central limit theorem. Inference chapters cover confidence intervals and significance tests for proportions, t intervals and t tests for means including matched pairs, errors and power, and chi-square tests for homogeneity and independence.

Chapters include workshops and project labs, such as an ethical A/B experiment and a school survey study, and close with multiple-choice and free-response revision. Optional sections that go beyond the AP course, including goodness-of-fit and inference for regression slope, are clearly marked and kept separate from exam content.

Look inside

Preview this book

Browse the full contents and read the opening topic.

13 chapters · 348 topics

00-The Power of Data - From Curiosity to Impact
1. Chapter 0 Overview Sample
2. 00.1 Why Statistics Matters - Part 1
3. 00.1 Why Statistics Matters - Part 2
4. 00.1 Why Statistics Matters - Part 3
5. 00.2 From Curiosity to Evidence - Part 1
6. 00.2 From Curiosity to Evidence - Part 2
7. 00.2 From Curiosity to Evidence - Part 3
8. 00.3 Projects That Show Your Thinking - Part 1
9. 00.3 Projects That Show Your Thinking - Part 2
10. 00.3 Projects That Show Your Thinking - Part 3
11. OPTION - Beyond AP Statistics: Useful to Learn, Not Examined
12. 00.4 Exam Playbook — Turn Knowledge into Clear Evidence
13. 00.5 Formula Field Guide — Know What Each Symbol Means
14. 00.6 Calculator Field Guide — Three Ways, One Statistical Argument
15. OPTION — Not AP Core, but Useful to Learn: Slope Inference
16. Revision (Multiple Choice)
17. Revision (Free Response)
01-Exploring One-Variable Data and Collecting Data
1. Chapter 1 Overview
2. 1.1 Introducing Statistics: What Can We Learn from Data? - Part 1
3. 1.1 Introducing Statistics: What Can We Learn from Data? - Part 2
4. 1.1 Introducing Statistics: What Can We Learn from Data? - Part 3
5. 1.2 Variables - Part 1
6. 1.2 Variables - Part 2
7. 1.2 Variables - Part 3
8. 1.3 Tabular Representation and Summary Statistics for One Categorical Variable - Part 1
9. 1.3 Tabular Representation and Summary Statistics for One Categorical Variable - Part 2
10. 1.3 Tabular Representation and Summary Statistics for One Categorical Variable - Part 3
11. 1.4 Graphical Representations for One Categorical Variable - Part 1
12. 1.4 Graphical Representations for One Categorical Variable - Part 2
13. 1.4 Graphical Representations for One Categorical Variable - Part 3
14. 1.5 Graphical Representations for One Quantitative Variable - Part 1
15. 1.5 Graphical Representations for One Quantitative Variable - Part 2
16. 1.5 Graphical Representations for One Quantitative Variable - Part 3
17. 1.6 Descriptions for One Quantitative Variable Distributions - Part 1
18. 1.6 Descriptions for One Quantitative Variable Distributions - Part 2
19. 1.6 Descriptions for One Quantitative Variable Distributions - Part 3
20. 1.7 Summary Statistics for One Quantitative Variable - Part 1
21. 1.7 Summary Statistics for One Quantitative Variable - Part 2
22. 1.7 Summary Statistics for One Quantitative Variable - Part 3
23. 1.8 Graphical Representations of Summary Statistics for One Quantitative Variable - Part 1
24. 1.8 Graphical Representations of Summary Statistics for One Quantitative Variable - Part 2
25. 1.8 Graphical Representations of Summary Statistics for One Quantitative Variable - Part 3
26. 1.9 Comparisons of the Distributions for One Quantitative Variable - Part 1
27. 1.9 Comparisons of the Distributions for One Quantitative Variable - Part 2
28. 1.9 Comparisons of the Distributions for One Quantitative Variable - Part 3
29. 1.10 The Investigative Question Revisited and Data Collection - Part 1
30. 1.10 The Investigative Question Revisited and Data Collection - Part 2
31. 1.10 The Investigative Question Revisited and Data Collection - Part 3
32. 1.11 Random Sampling - Part 1
33. 1.11 Random Sampling - Part 2
34. 1.11 Random Sampling - Part 3
35. 1.12 Potential Problems with Sampling - Part 1
36. 1.12 Potential Problems with Sampling - Part 2
37. 1.12 Potential Problems with Sampling - Part 3
38. 1.13 Experimental Design - Part 1
39. 1.13 Experimental Design - Part 2
40. 1.13 Experimental Design - Part 3
41. OPTION - Beyond AP Statistics: Useful to Learn, Not Examined
42. Revision (Multiple Choice)
43. Revision (Free Response)
02-Probability, Random Variables, and Probability Distributions
1. Chapter 2 Overview
2. 2.1 Tabular and Graphical Representations for the Distributions of Two Categorical Variables - Part 1
3. 2.1 Tabular and Graphical Representations for the Distributions of Two Categorical Variables - Part 2
4. 2.1 Tabular and Graphical Representations for the Distributions of Two Categorical Variables - Part 3
5. 2.2 Summary Statistics for Two Categorical Variables - Part 1
6. 2.2 Summary Statistics for Two Categorical Variables - Part 2
7. 2.2 Summary Statistics for Two Categorical Variables - Part 3
8. 2.3 Estimating Probabilities Using Simulation - Part 1
9. 2.3 Estimating Probabilities Using Simulation - Part 2
10. 2.3 Estimating Probabilities Using Simulation - Part 3
11. 2.4 Introduction to Probability - Part 1
12. 2.4 Introduction to Probability - Part 2
13. 2.4 Introduction to Probability - Part 3
14. 2.5 Mutually Exclusive Events - Part 1
15. 2.5 Mutually Exclusive Events - Part 2
16. 2.5 Mutually Exclusive Events - Part 3
17. 2.6 Conditional Probability - Part 1
18. 2.6 Conditional Probability - Part 2
19. 2.6 Conditional Probability - Part 3
20. 2.7 Independent Events and Unions of Events - Part 1
21. 2.7 Independent Events and Unions of Events - Part 2
22. 2.7 Independent Events and Unions of Events - Part 3
23. 2.8 Introduction to Random Variables and Probability Distributions - Part 1
24. 2.8 Introduction to Random Variables and Probability Distributions - Part 2
25. 2.8 Introduction to Random Variables and Probability Distributions - Part 3
26. 2.9 Parameters of Random Variables - Part 1
27. 2.9 Parameters of Random Variables - Part 2
28. 2.9 Parameters of Random Variables - Part 3
29. 2.10 The Binomial Distribution - Part 1
30. 2.10 The Binomial Distribution - Part 2
31. 2.10 The Binomial Distribution - Part 3
32. 2.11 The Normal Distribution - Part 1
33. 2.11 The Normal Distribution - Part 2
34. 2.11 The Normal Distribution - Part 3
35. 2.12 Sampling Distributions and the Central Limit Theorem - Part 1
36. 2.12 Sampling Distributions and the Central Limit Theorem - Part 2
37. 2.12 Sampling Distributions and the Central Limit Theorem - Part 3
38. OPTION - Beyond AP Statistics: Useful to Learn, Not Examined
39. Revision (Multiple Choice)
40. Revision (Free Response)
03-Probability, Random Variables, 3nd Probability Distributions
1. Chapter 3 Overview
2. 3.1 Estimators - Part 1
3. 3.1 Estimators - Part 2
4. 3.1 Estimators - Part 3
5. 3.2 Sampling Distributions for Sample Proportions - Part 1
6. 3.2 Sampling Distributions for Sample Proportions - Part 2
7. 3.2 Sampling Distributions for Sample Proportions - Part 3
8. 3.3 Constructing a Confidence Interval for a Population Proportion - Part 1
9. 3.3 Constructing a Confidence Interval for a Population Proportion - Part 2
10. 3.4 Justifying a Claim Based on a Confidence Interval for a Population Proportion - Part 1
11. 3.4 Justifying a Claim Based on a Confidence Interval for a Population Proportion - Part 2
12. 3.5 Setting Up a Test for a Population Proportion - Part 1
13. 3.5 Setting Up a Test for a Population Proportion - Part 2
14. 3.6 p-Values - Part 1
15. 3.6 p-Values - Part 2
16. 3.7 Carrying Out a Test for a Population Proportion - Part 1
17. 3.7 Carrying Out a Test for a Population Proportion - Part 2
18. 3.8 Potential Errors When Performing Tests - Part 1
19. 3.8 Potential Errors When Performing Tests - Part 2
20. 3.9 Sampling Distributions for the Difference Between Sample Proportions - Part 1
21. 3.9 Sampling Distributions for the Difference Between Sample Proportions - Part 2
22. 3.10 Constructing a Confidence Interval for the Difference Between Two Population Proportions - Part 1
23. 3.10 Constructing a Confidence Interval for the Difference Between Two Population Proportions - Part 2
24. 3.11 Justifying a Claim Based on a Confidence Interval for the Difference Between Two Population Proportions - Part 1
25. 3.11 Justifying a Claim Based on a Confidence Interval for the Difference Between Two Population Proportions - Part 2
26. 3.12 Setting Up a Test for the Difference Between Two Population Proportions - Part 1
27. 3.12 Setting Up a Test for the Difference Between Two Population Proportions - Part 2
28. 3.13 Carrying Out a Test for the Difference Between Two Population Proportions - Part 1
29. 3.13 Carrying Out a Test for the Difference Between Two Population Proportions - Part 2
30. 3.14 Setting Up a Chi-Square Test for Homogeneity or Independence - Part 1
31. 3.14 Setting Up a Chi-Square Test for Homogeneity or Independence - Part 2
32. 3.15 Carrying Out a Chi-Square Test for Homogeneity or Independence - Part 1
33. 3.15 Carrying Out a Chi-Square Test for Homogeneity or Independence - Part 2
34. 4.1 Sampling Distributions for Sample Means - Part 1
35. 4.1 Sampling Distributions for Sample Means - Part 2
36. 4.2 Constructing a Confidence Interval for a Population Mean or Population Mean Difference - Part 1
37. 4.2 Constructing a Confidence Interval for a Population Mean or Population Mean Difference - Part 2
38. 4.3 Justifying a Claim Based on a Confidence Interval for a Population Mean or Population Mean Difference - Part 1
39. 4.3 Justifying a Claim Based on a Confidence Interval for a Population Mean or Population Mean Difference - Part 2
40. 4.4 Setting Up a Test for a Population Mean or Population Mean Difference - Part 1
41. 4.4 Setting Up a Test for a Population Mean or Population Mean Difference - Part 2
42. 4.5 Carrying Out a Test for a Population Mean or Population Mean Difference - Part 1
43. 4.5 Carrying Out a Test for a Population Mean or Population Mean Difference - Part 2
44. 4.6 Sampling Distributions for the Difference Between Two Sample Means - Part 1
45. 4.6 Sampling Distributions for the Difference Between Two Sample Means - Part 2
46. 4.7 Constructing a Confidence Interval for the Difference Between Two Population Means - Part 1
47. 4.7 Constructing a Confidence Interval for the Difference Between Two Population Means - Part 2
48. 4.8 Justifying a Claim Based on a Confidence Interval for the Difference Between Two Population Means - Part 1
49. 4.8 Justifying a Claim Based on a Confidence Interval for the Difference Between Two Population Means - Part 2
50. 4.9 Setting Up a Test for the Difference Between Two Population Means - Part 1
51. 4.9 Setting Up a Test for the Difference Between Two Population Means - Part 2
52. 4.10 Carrying Out a Test for the Difference Between Two Population Means - Part 1
53. 4.10 Carrying Out a Test for the Difference Between Two Population Means - Part 2
54. 5.1 Graphical Representations Between Two Quantitative Variables - Part 1
55. 5.1 Graphical Representations Between Two Quantitative Variables - Part 2
56. 5.2 Correlation - Part 1
57. 5.2 Correlation - Part 2
58. 5.3 Linear Regression Models - Part 1
59. 5.3 Linear Regression Models - Part 2
60. 5.4 Residuals - Part 1
61. 5.4 Residuals - Part 2
62. 5.5 Least-Squares Regression - Part 1
63. 5.5 Least-Squares Regression - Part 2
64. Project Lab - One Question, Three Types of Evidence
65. OPTION - Beyond AP Statistics: Useful to Learn, Not Examined
66. Revision (Multiple Choice)
67. Revision (Free Response)
04-Collecting Data - From Questions to Trustworthy Evidence
1. Chapter 4 Overview
2. 1.10 The Investigative Question Revisited and Data Collection - Part 1
3. 1.10 The Investigative Question Revisited and Data Collection - Part 2
4. 1.10 The Investigative Question Revisited and Data Collection - Part 3
5. 1.11 Random Sampling - Part 1
6. 1.11 Random Sampling - Part 2
7. 1.11 Random Sampling - Part 3
8. 1.12 Potential Problems with Sampling - Part 1
9. 1.12 Potential Problems with Sampling - Part 2
10. 1.12 Potential Problems with Sampling - Part 3
11. 1.13 Experimental Design - Part 1
12. 1.13 Experimental Design - Part 2
13. 1.13 Experimental Design - Part 3
14. Source Workshop: Make Every Design Reproducible
15. Project Lab: A Study Worth Explaining
16. Unit 4 Continuation: Using Studies Wisely
17. OPTION - Beyond AP Statistics: Useful to Learn, Not Examined
18. Revision (Multiple Choice)
19. Revision (Free Response)
05-Probability — Making Sense of Chance
1. Chapter 5 Overview
2. 2.3 Estimating Probabilities Using Simulation - Part 1
3. 2.3 Estimating Probabilities Using Simulation - Part 2
4. 2.3 Estimating Probabilities Using Simulation - Part 3
5. 2.4 Introduction to Probability - Part 1
6. 2.4 Introduction to Probability - Part 2
7. 2.4 Introduction to Probability - Part 3
8. 2.5 Mutually Exclusive Events - Part 1
9. 2.5 Mutually Exclusive Events - Part 2
10. 2.5 Mutually Exclusive Events - Part 3
11. 2.6 Conditional Probability - Part 1
12. 2.6 Conditional Probability - Part 2
13. 2.6 Conditional Probability - Part 3
14. 2.7 Independent Events and Unions of Events - Part 1
15. 2.7 Independent Events and Unions of Events - Part 2
16. 2.7 Independent Events and Unions of Events - Part 3
17. Source Workshop: From Sample Spaces to Complete Simulations
18. Project Lab: How Many Reservations Should We Accept?
19. OPTION - Beyond AP Statistics: Useful to Learn, Not Examined
20. Revision (Multiple Choice)
21. Revision (Free Response)
06-Random Variables — Modelling Uncertainty
1. Chapter 6 Overview
2. 2.8 Introduction to Random Variables and Probability Distributions - Part 1
3. 2.8 Introduction to Random Variables and Probability Distributions - Part 2
4. 2.8 Introduction to Random Variables and Probability Distributions - Part 3
5. 2.9 Parameters of Random Variables - Part 1
6. 2.9 Parameters of Random Variables - Part 2
7. 2.9 Parameters of Random Variables - Part 3
8. 2.10 The Binomial Distribution - Part 1
9. 2.10 The Binomial Distribution - Part 2
10. 2.10 The Binomial Distribution - Part 3
11. 2.11 The Normal Distribution - Part 1
12. 2.11 The Normal Distribution - Part 2
13. 2.11 The Normal Distribution - Part 3
14. Calculator Workshop: Show the Distribution, Then Calculate
15. Source Completion Workshop - Building and Comparing Distributions
16. Project Lab: Same Average, Different Risk
17. OPTION - Beyond AP Statistics: Useful to Learn, Not Examined - Part 1
18. OPTION - Beyond AP Statistics: Useful to Learn, Not Examined - Part 2
19. OPTION - Beyond AP Statistics: Useful to Learn, Not Examined - Part 3
20. OPTION Practice - Enrichment Only
21. Revision (Multiple Choice)
22. Revision (Free Response)
07-Sampling Distributions - From Samples to Evidence
1. Chapter 7 Overview
2. 2.12 Sampling Distributions: One Statistic from Each Sample - Part 1
3. 2.12 Sampling Distributions: One Statistic from Each Sample - Part 2
4. 3.1 Estimators: Bias, Variability and Accuracy - Part 1
5. 3.1 Estimators: Bias, Variability and Accuracy - Part 2
6. 3.2 Sampling Distributions for Sample Proportions - Part 1
7. 3.2 Sampling Distributions for Sample Proportions - Part 2
8. 3.9 Differences Between Sample Proportions - Part 1
9. 3.9 Differences Between Sample Proportions - Part 2
10. 4.1 Sample Means and the Central Limit Theorem - Part 1
11. 4.1 Sample Means and the Central Limit Theorem - Part 2
12. 4.6 Differences Between Sample Means - Part 1
13. 4.6 Differences Between Sample Means - Part 2
14. Calculator Workshop - Simulate Before You Approximate
15. Project Lab - How Stable Is Our School Survey?
16. OPTION - Not Required for AP Statistics, but Useful to Learn
17. Revision (Multiple Choice)
18. Revision (Free Response)
08-Estimating Proportions with Confidence
1. Chapter 8 Overview
2. 3.3 Constructing a Confidence Interval for a Population Proportion - Part 1
3. 3.3 Constructing a Confidence Interval for a Population Proportion - Part 2
4. 3.3 Constructing a Confidence Interval for a Population Proportion - Part 3
5. 3.4 Justifying a Claim Based on a Confidence Interval for a Population Proportion - Part 1
6. 3.4 Justifying a Claim Based on a Confidence Interval for a Population Proportion - Part 2
7. 3.4 Justifying a Claim Based on a Confidence Interval for a Population Proportion - Part 3
8. 3.10 Constructing a Confidence Interval for the Difference Between Two Population Proportions - Part 1
9. 3.10 Constructing a Confidence Interval for the Difference Between Two Population Proportions - Part 2
10. 3.10 Constructing a Confidence Interval for the Difference Between Two Population Proportions - Part 3
11. 3.11 Justifying a Claim Based on a Confidence Interval for a Difference in Proportions - Part 1
12. 3.11 Justifying a Claim Based on a Confidence Interval for a Difference in Proportions - Part 2
13. 3.11 Justifying a Claim Based on a Confidence Interval for a Difference in Proportions - Part 3
14. Simulation Workshop - What Does 95% Capture?
15. Project Lab - Is Support Strong Enough to Act?
16. OPTION - Not Required for AP Statistics, but Useful to Learn
17. Revision (Multiple Choice)
18. Revision (Free Response)
09-Testing Claims About Proportions - Evidence Before Conclusions
1. Chapter 9 Overview
2. 3.5 Setting Up a Test for a Population Proportion - Part 1
3. 3.5 Setting Up a Test for a Population Proportion - Part 2
4. 3.6 p-Values - Part 1
5. 3.6 p-Values - Part 2
6. 3.7 Carrying Out a Test for a Population Proportion - Part 1
7. 3.7 Carrying Out a Test for a Population Proportion - Part 2
8. 3.8 Potential Errors When Performing Tests - Part 1
9. 3.8 Potential Errors When Performing Tests - Part 2
10. 3.12 Setting Up a Test for the Difference Between Two Population Proportions - Part 1
11. 3.12 Setting Up a Test for the Difference Between Two Population Proportions - Part 2
12. 3.13 Carrying Out a Test for the Difference Between Two Population Proportions - Part 1
13. 3.13 Carrying Out a Test for the Difference Between Two Population Proportions - Part 2
14. Simulation Workshop - Build the Null Distribution
15. Project Lab - An Ethical A/B Reminder Experiment
16. Cross-Chapter Connection - Regression Output Is Evidence with Limits
17. OPTION - Not Required AP Procedures, but Useful to Learn
18. Revision (Multiple Choice)
19. Revision (Free Response)
10-Estimating Means with Confidence - A Range Worth Trusting
1. Chapter 10 Overview
2. 4.2 From z to t: Critical Values and Conditions - Part 1
3. 4.2 From z to t: Critical Values and Conditions - Part 2
4. 4.2 Constructing a One-Sample t Interval - Part 1
5. 4.2 Constructing a One-Sample t Interval - Part 2
6. 4.3 Interpreting Confidence and Controlling Precision - Part 1
7. 4.3 Interpreting Confidence and Controlling Precision - Part 2
8. 4.7 Estimating the Difference Between Independent Means - Part 1
9. 4.7 Estimating the Difference Between Independent Means - Part 2
10. 4.8 Reading Difference Intervals Without Overclaiming - Part 1
11. 4.8 Reading Difference Intervals Without Overclaiming - Part 2
12. 4.2 / 4.3 Matched Pairs: One Sample of Differences - Part 1
13. 4.2 / 4.3 Matched Pairs: One Sample of Differences - Part 2
14. Procedure Choice and Sampling-Distribution Bridge
15. Simulation Workshop - Does 95% Mean 95%?
16. Case Study Project - Accessible Search, Measurable Improvement
17. OPTION - Not AP Statistics Core, but Useful to Learn
18. AP Revision - 15 Mixed MCQs
19. AP-Style Free Response - Explain the Design and the Interval
20. Source Review Bridge - Probability and Causal Claims
11-Testing Claims About Means - Does the Difference Hold Up?
1. Chapter 11 Overview
2. 4.4 Setting Up a Test for a Population Mean - Part 1
3. 4.4 Setting Up a Test for a Population Mean - Part 2
4. 4.5 Carrying Out a One-Sample t Test - Part 1
5. 4.5 Carrying Out a One-Sample t Test - Part 2
6. 4.4 / 4.5 Matched Pairs: Testing the Mean Difference - Part 1
7. 4.4 / 4.5 Matched Pairs: Testing the Mean Difference - Part 2
8. 4.9 Setting Up a Test for Two Independent Means - Part 1
9. 4.9 Setting Up a Test for Two Independent Means - Part 2
10. 4.10 Carrying Out a Two-Sample t Test - Part 1
11. 4.10 Carrying Out a Two-Sample t Test - Part 2
12. 4.5 / 4.10 Decisions, Errors, Power and Practical Importance - Part 1
13. 4.5 / 4.10 Decisions, Errors, Power and Practical Importance - Part 2
14. Choose the Procedure Before You Calculate
15. Randomization Workshop - Build a No-Effect Model
16. Case Study Project - Which Revision Format Helps?
17. OPTION - Not AP Statistics Core, but Useful to Learn
18. AP Revision - 15 Mixed MCQs
19. AP-Style Free Response - Three Complete Investigations
20. Final Reasoning Check
12-Patterns, Proportions and Relationships - Evidence Beyond the Average
1. Chapter 12 Overview
2. 3.14 Chi-Square Models: Counts, Shape and Conditions - Part 1
3. 3.14 Chi-Square Models: Counts, Shape and Conditions - Part 2
4. 3.14 Setting Up a Test for Homogeneity - Part 1
5. 3.14 Setting Up a Test for Homogeneity - Part 2
6. 3.14 Setting Up a Test for Independence - Part 1
7. 3.14 Setting Up a Test for Independence - Part 2
8. 3.15 Expected Counts and a Complete Chi-Square Calculation - Part 1
9. 3.15 Expected Counts and a Complete Chi-Square Calculation - Part 2
10. 3.15 Interpreting the Result and Following Up Responsibly - Part 1
11. 3.15 Interpreting the Result and Following Up Responsibly - Part 2
12. OPTION - Not AP Core: OPTION Goodness-of-Fit: A Claimed Categorical Distribution - Part 1
13. OPTION - Not AP Core: OPTION Goodness-of-Fit: A Claimed Categorical Distribution - Part 2
14. OPTION - Not AP Core: OPTION Inference for Regression Slope: The t Test - Part 1
15. OPTION - Not AP Core: OPTION Inference for Regression Slope: The t Test - Part 2
16. OPTION - Not AP Core: OPTION Slope Confidence Intervals and Responsible Prediction - Part 1
17. OPTION - Not AP Core: OPTION Slope Confidence Intervals and Responsible Prediction - Part 2
18. AP Core Workshop - Small Expected Counts and Randomization
19. Case Study Project - Survey Wording and Student Choices
20. AP Revision - 15 Mixed MCQs (Current Scope Only)
21. AP-Style FRQ 1 - Association in a District
22. AP-Style FRQ 2 - When the Approximation Fails
23. OPTION FRQ - Distribution Fit and Slope Inference
24. One-Page Method Map

Sample · 00-The Power of Data - From Curiosity to Impact

Chapter 0 Overview

CHAPTER 00: The Power of Data - From Curiosity to Impact

This introduction explains why statistical thinking matters, how the revised AP Statistics course is organized, and how an original student project can grow from a question into defensible evidence. It is a book orientation chapter, not a sixth College Board unit. Three introductory topics connect ideas to career examples and potential US university application experiences. The questions preview foundational reasoning; later chapters teach the formal methods in full.

🎓 Where This Chapter Can Take You

Mira, a fictional student, notices that her school is planning to change its lunch service. One friend says the queues are unbearable. Another says the system works perfectly. Both can remember a convincing example. Mira wants to know whether the disagreement reflects different experiences, different definitions, or simply the lunches each friend happens to remember.

She begins with a notebook, not a complicated model. What counts as waiting time? When does the clock start? Which days and lunch periods should be included? A teacher asks her to write those decisions before collecting data. Her first pilot reveals a problem: one observer stops the timer when a student reaches the counter, while another stops it when the student receives food. The numbers looked comparable, but they were measuring different things.

Mira revises the protocol and tries again. Now she can describe variation, explain how observations were obtained, and separate a practical suggestion from a claim she cannot support. Her project is still small. What makes it valuable is that her reasoning is visible. The same habits matter in environmental monitoring, business research, engineering, and public policy. A university project or future workplace investigation begins with this discipline: ask a useful question, obtain appropriate evidence, and tell the reader what remains uncertain.

Career	What they do	Topics used	Example university programs
Environmental data analyst	Write a question about an observable school process.	1.1	Statistics, UC Berkeley
Market research analyst	Design one single-response question with nonoverlapping options.	1.3	Statistics, METU
Sports performance analyst	Write a four-feature description of a measured distribution.	1.6	Statistics, METU
Public transport planner	Compare the same measurement across two groups.	1.9	Statistics, METU
Survey methodologist	Create a sampling frame for an accessible group.	1.11	Statistics and Data Science, Carnegie Mellon University

Characters, studies, and numerical datasets in this book are fictional teaching examples. University programs are real examples, not endorsements or admissions promises. Content and assessment follow the revised 2026-27 framework. OPTION sections are enrichment and are excluded from AP revision questions.

The course at a glanceUnitContentMCQ weighting
1Exploring One-Variable Data and Collecting Data20-30%
2Probability, Random Variables, and Probability Distributions15-25%
3Inference for Categorical Data - Proportions15-25%
4Inference for Quantitative Data - Means10-20%
5Regression Analysis10-20%

Continue reading — 13 chapters, worked examples and practice.

Built for AP preparation

Topics follow the AP syllabus with visual explanations and worked solutions close to the lesson content.

Readable in the portal

The book opens in the VerityPrep reader with chapter navigation, search and reading controls.

Share-ready link

https://www.verityprep.com/books/ap-statistics-power-of-data

Unit	Content	MCQ weighting
1	Exploring One-Variable Data and Collecting Data	20-30%
2	Probability, Random Variables, and Probability Distributions	15-25%
3	Inference for Categorical Data - Proportions	15-25%
4	Inference for Quantitative Data - Means	10-20%
5	Regression Analysis	10-20%