Features

A New Approach to Probability: Research has demonstrated how students develop an understanding of probability and chance. Using natural frequencies to reason about probability, especially conditional probability, is much easier for students to understand. The treatment of probability in this text is complete, including conditional probability and Bayes' Rule type probability calculations. However, it's done in a way that eliminates the need for the symbolism and formulas, which are a roadblock for many students.

Chapter on Overview of Statistical Inference (Chapter 7): This short chapter focuses on the things students need to think about in order to select an appropriate method of analysis. In most texts, these considerations are "hidden" in the discussion that occurs when a new method is introduced. Discussing these considerations up front in the form of four key questions that need to be answered before choosing an inference method makes it easier for students to make correct choices.

An Organization That Reflects the Data Analysis Process: Students are introduced early to the idea that data analysis is a process that begins with careful planning, followed by data collection, data description using graphical and numerical summaries, data analysis, and finally interpretation of results. The ordering of topics in the textbook mirrors this process: data collection, then data description, then statistical inference.

Inference for Proportions before Inference for Means: The book makes it possible to develop the concept of a sampling distribution via simulation. Simulation is simpler in the context of proportions, where it is easy to construct a hypothetical population (versus the more complicated context of means, which requires assumptions about shape and spread). In addition, inferential procedures for proportions are based on the normal distribution, allowing students to focus on the new concepts of estimation and hypothesis testing without having to grapple with the introduction of the t distribution.

Separate Treatment of Inference Based on Experiment Data (Chapter 14): Many statistical studies involve collecting data via experimentation. The same inference procedures used to estimate or test hypotheses about population parameters also are used to estimate or test hypotheses about treatment effects. However, the necessary assumptions are slightly different (for example, random assignment replaces the assumption of random selection), as is the wording of conclusions. Treating both cases together tends to confuse students; this text makes the distinction clear.

Chapter Learning Objectives--Keeping Students Informed about Expectations: The learning objectives explicitly state the expected student outcomes, and are presented in three categories: Conceptual Understanding, Mastery of Mechanics, and Putting It into Practice.

Preview--Motivation for Learning: Each chapter opens with a Preview and Preview Example that provide motivation for studying the concepts and methods introduced in the chapter. They address why the material is worth learning, provide the conceptual foundation for the methods covered in the chapter, and connect to what the student already knows. These relevant and current examples provide a context in which one or more questions are proposed for further investigation. The context is revisited in the chapter once students have the necessary understanding to more fully address the questions posed.

Real Data That Motivates and Engages: Examples and exercises with overly simple settings don't allow students to practice interpreting results in real situations. The exercises and examples are a particular strength of this text. Most involve data extracted from journal articles, newspapers, and other published sources. They cover a wide range of disciplines and subject areas of interest to today's students, including, among others, health and fitness, consumer research, psychology and aging, environmental research, law and criminal justice, and entertainment.

Exercises Organized into Developmental Sets to Structure the Out-of-Class Experience: End-of-section exercises are presented in two developmental sets. The exercises in each set work together to assess all of the learning objectives for the section. Additional section exercises are included for those who want more practice.

Are You Ready to Move On?--Students Test Their Understanding: Prior to moving to the next chapter, "Are You Ready to Move On?" questions allow students to confirm that they have achieved the chapter learning objectives. Like the problem sets for each section, this collection of exercises is developmental--assessing all of the chapter learning objectives and serving as a comprehensive end-of-chapter review.

Exploring the Big Ideas--Real-Data Algorithmic Sampling Exercises: Most chapters contain extended sampling-based, real-data exercises at the end of the chapter. Each student goes to the companion website *(http://www.xxx.com)* and gets a different random sample of data from a population. The student then uses that sample to answer the questions. These unique exercises are designed to teach about sampling variability and provide a vehicle for rich classroom discussions of this important statistical concept.

Simple Design: Recent research shows that many of the "features" in current textbooks are not really helpful to students. In fact, cartoons, sidebars, historical notes, fake post-it notes in the margins, etc. actually distract students and interfere with learning. STATISTICS: LEARNING FROM DATA has a simple, clean design that minimizes clutter and maximizes student understanding.

Chapter Activities--Engaging Students in Hands-On Activities: There is a growing body of evidence indicating that students learn best when they are actively engaged. Chapter activities guide students' thinking about important ideas and concepts.