160 pages | 22 B/W Illus.
Americans are bombarded with statistical data each and every day, and healthcare professionals are no exception. All segments of healthcare rely on data provided by insurance companies, consultants, research firms, and the federal government to help them make a host of decisions regarding the delivery of medical services. But while these health professionals rely on data, do they really make the best use of the information? Not if they fail to understand whether the assumptions behind the formulas generating the numbers make sense. Not if they don’t understand that the world of healthcare is flooded with inaccurate, misleading, and even dangerous statistics.
Statistical Analysis for Decision Makers in Healthcare: Understanding and Evaluating Critical Information in a Competitive Market, Second Edition explains the fundamental concepts of statistics, as well as their common uses and misuses. Without jargon or mathematical formulas, nationally renowned healthcare expert and author, Jeff Bauer, presents a clear verbal and visual explanation of what statistics really do. He provides a practical discussion of scientific methods and data to show why statistics should never be allowed to compensate for bad science or bad data.
Relying on real-world examples, Dr. Bauer stresses a conceptual understanding that empowers readers to apply a scientifically rigorous approach to the evaluation of data. With the tools he supplies, you will learn how to dismantle statistical evidence that goes against common sense. Easy to understand, practical, and even entertaining, this is the book you wish you had when you took statistics in college — and the one you are now glad to have to defend yourself against the abundance of bad studies and misinformation that might otherwise corrupt your decisions.
Section I. The Scientific Foundations of Statistical Analysis
Scientific Method: The language of statistical studies
Experimental design: the foundation of statistical conclusions
Section II. The Fundamental Importance of Data
Numbers Good and Bad: How to judge the quality of data
Samples and Surveys: How numbers should be collected
Section III. The Different Types of Statistics
Descriptive Statistics: The foundation of comparisons
Inferential Statistics: Studies of differences
Relational Statistics: Studies of relationships
Explanatory Statistics: Studies of causality
Postscript. Statistics in Perspective