Chapter 2 · Displaying and Summarizing Categorical Data

Section 2.2Drawing and Interpreting Pie Charts

Pie charts help you compare how a whole is divided among different categories. In this section, you'll learn when pie charts are appropriate, how they are constructed from relative frequencies, and how to interpret the information they display so you can communicate data clearly and accurately.

Summary

Pie chartVisually represents the proportion of categories in a dataset.

Sector sizeTo calculate the size of each sector, multiply the relative frequency by 360 degrees.

ProtractorUsed to accurately measure and draw sectors.

Area principleEnsures that the chart accurately reflects data proportions — each sector's area must be proportional to its relative frequency.

Pie charts are most effective when comparing a small number of categories that together make up a meaningful whole.

The Real World Statistics Learning Progression

Each section follows the same learning progression: understand the concept, practice the skill, think like a statistician, then apply your learning to authentic data.

Learn This Section

Whether learning independently or teaching a class, the learning path below is designed to help students master the concepts in this section. Page references correspond to the Real World Statistics instructional system.

Read the sectionRead pages 30–33 to reinforce the concepts introduced in the lesson.

Practice what you've learnedComplete the 2 multi-part practice questions on pages 33–34. Check your work using the Answer Key, then review and correct any questions you missed before moving on.

Think like a statisticianExplore Enrichment: Role-Based Statistical Analysis Tasks and the Chapter Dataset to apply statistical thinking in real-world contexts.

Need Extra Support? If you'd like additional guidance while working through the practice questions, explore the Worked Solutions Manual for step-by-step solutions and explanations.

From Learners to Statisticians

Examples of authentic statistical work supported by this section include:

Organizing raw categorical responses (e.g., voter polls, school surveys, customer choice tallies) into frequency and relative frequency tables to reveal how a group is distributed.
Converting categorical counts (e.g., streaming preferences from a student poll, service usage logs from a community survey) into proportional displays such as pie charts that show part-to-whole structure.
Interpreting categorical distributions (e.g., transportation choices from commuter data, purchase patterns from retail records) to explain what categories dominate or are least represented in context.

You may use these ideas to design your own investigations or explore the Real World Statistics System.

Enrichment: Role-Based Statistical Analysis Tasks

Enrichment tasks place students in the role of a statistician working on real research problems, where they evaluate data, justify decisions, and communicate findings clearly. Each section includes multiple enrichment tasks, including one with a model response that demonstrates statistical reasoning in context, showing how evidence is evaluated, decisions are justified, and conclusions are communicated. This serves as a bridge to working with authentic datasets and independent statistical investigation within each chapter.

Available in the Interactive eBook at the end of the section (page 34) and in the Appendix of the Print Book (page 401).

Teaching with Real World Statistics?

These structured learning paths — including Enrichment tasks and Chapter Datasets — can be adapted or copied directly into Canvas, Google Classroom, Schoology, and other learning management systems to simplify lesson planning and student assignments.

Part of the Real World Statistics instructional system — bringing together print, interactive learning, videos, datasets, enrichment, worked solutions, and teacher resources.