**KEY MESSAGE**: The **Tenth Edition** of this highly-regarded introductory text emphasizes inference and sound decision-making through its extensive coverage of data collection and analysis. McClave develops statistical thinking and teaches readers to properly assess the credibility of inferences-from the vantage point of both the consumer and the producer. This edition incorporates more exercises and more visual features, such as redesigned end-of-chapter summaries and an increased use of applets. This text assumes a mathematical background of basic algebra.

**KEY TOPICS**: Statistics, Data, and Statistical Thinking; Methods for Describing Sets of Data; Probability (from McClave 11e-Chap 3); Random Variables and Probability Distributions; Inferences Based on a Single Sample: Estimation with Confidence Intervals; Inferences Based on a Single Sample: Tests of Hypothesis; Comparing Population Means; Comparing Population Proportions; Simple Linear Regression

**MARKET**: For all readers interested in statistics.

*"synopsis" may belong to another edition of this title.*

This book presents balanced coverage of the theory and application of statistics while enhancing student's critical thinking skills. It teaches students how to analyze data that appear in situations in the world around them - almost all of the examples and exercises are based on current, real-world applications pulled from journals, magazines, news articles, and commerce. The text shows students that knowledge of statistics is important to their own lives.

Key Benefit: This book presents balanced coverage of the theory and application of statistics while enhancing reader's critical thinking skills. Key Topics: This book shows readers that knowledge of statistics is important to their own lives. Almost all of the examples and exercises are based on current, real-world applications pulled from journals, magazines, news articles, and commerce. Presents the importance of data collection, observations, experiments, and surveys in drawing meaningful inferences from data. Now provides an equal balance between applications relying on p-values and those relying on critical values in their interpretation.

*"About this title" may belong to another edition of this title.*

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