Statistical Thinking through Media Examples uses real-world examples from various media to give students an introduction to fundamentals of statistical thinking. Unlike many standard texts in the discipline, the book focuses on conceptual understanding - the meaning behind mathematical calculations rather than the calculations themselves. Written in accessible language, the book begins by discussing the importance of learning how to assess the quality of research results presented by the media. This understanding creates an essential context for subsequent chapters on questioning study design including polls and surveys, reasoning with variation in data measurements, understanding probability, confidence intervals, hypothesis testing, and linear regression. Students also learn how statistics can be misused and manipulated by researchers to provide a desired result. The second edition features new coverage of select topics, including ANOVA, Chi-Square tests, and multiple linear regression. Every chapter references online resources or includes in-depth discussion to draw connections between statistical concepts and media examples, such as studies on alcohol consumption and cardiovascular health, American football and brain injury, public mass shootings, dishonesty in scientific research, and more. Statistical Thinking through Media Examples is an ideal resource for any course that deals with introductory statistics, particularly those in the health and social sciences, journalism, and business. Anthony Donoghue earned his master's degree in statistics at University College Dublin and has worked as a professional statistician in both Ireland and the United States. Currently, he works as a consultant in New York, providing statistics and programming expertise and training. He is also a statistics faculty member at both Columbia University and New York University, where he teaches statistical computing, statistics, and statistical thinking.

Uses real-world examples from various media to give students an introduction to fundamentals of statistical thinking. Unlike many standard texts in the discipline, the book focuses on conceptual understanding - the meaning behind mathematical calculations rather than the calculations themselves.

This book discusses conceptual and pragmatic issues in the assessment of statistical knowledge and reasoning skills among students at the college and precollege levels, and the use of assessments to improve instruction. It is designed primarily for academic audiences involved in teaching statistics and mathematics, and in teacher education and training. The book is divided in four sections: (I) Assessment goals and frameworks, (2) Assessing conceptual understanding of statistical ideas, (3) Innovative models for classroom assessments, and (4) Assessing understanding of probability.

Nontechnical survey helps improve ability to judge statistical evidence and to make better-informed decisions. Discusses common pitfalls: unrealistic estimates, improper comparisons, premature conclusions, and faulty thinking about probability. 1974 edition.

How statistical thinking and methodology can help you make crucial business decisions Straightforward and insightful, Statistical Thinking: Improving Business Performance, Second Edition, prepares you for business leadership by developing your capacity to apply statistical thinking to improve business processes. Unique and compelling, this book shows you how to derive actionable conclusions from data analysis, solve real problems, and improve real processes. Here, you'll discover how to implement statistical thinking and methodology in your work to improve business performance. Explores why statistical thinking is necessary and helpful Provides case studies that illustrate how to integrate several statistical tools into the decision-making process Facilitates and encourages an experiential learning environment to enable you to apply material to actual problems With an in-depth discussion of JMP® software, the new edition of this important book focuses on skills to improve business processes, including collecting data appropriate for a specified purpose, recognizing limitations in existing data, and understanding the limitations of statistical analyses.