Stat-Spotting: A Field Guide to Identifying Dubious Data

Statistics are used to support a variety of claims. It takes a trained eye to interpret the validity of a massive amount of statistical data particularly surrounding health care claims. Statistics are not synonymous with facts. Although statistics play a major role in data integrity one must look closer with a dubious eye. Especially since statistics are often used to support a claim or sell a concept. Joel Best takes the most common data marketing tricks and explains them to the lay person in a creative way. The dubious data is laid out before the reader as one would expect to see in an ecology field guide for nature enthusiasts. Therefore, Stat-Spotting fulfills its mission delightfully as promised by presenting a guide for spotting dubious data, "questionable stats". Along with the creative format the author also appeals to the reader's sense of reporting order by covering the "how", the "who" and the "what". Be on the look out for "fictoids" which the author defines as colorful or erroneous stats used as a hyperbole. Where upon discovery a loud bell should ring in one's head as a reminder to not take literally but instead look deeper. In order to master stat spotting, here are a few simple rules to keep in mind: 1. Having a sense of scale allows you to understand the magnitude and validity of data. 2. The more severe or dramatic the case the more likely it is to be extremely rare. 3. Most people are innumerate or mathematically illiterate and subsequently easily fooled. 4. Keep an eye out for numbers that are surprising large or small. 5. The unit of measure is deceiving, for example using minutes to report a crime rate (% of total population) 6. All stats should be reported in simple language. The language used may change the implication of the data. 7. The graph is a visual representation of the statistic and may be misleading. 8. If the number is too high or too low, it most likely errs on the side of exaggeration and is therefore a guestimate. Think about how it was calculated. 9. Watch for superlatives by their nature they imply comparison. 10. Since every stat implies a definition realize the ever changing nature of definitions, for example "overweight" 11. Methodologies of data collection affect the outcome. 12. The "who" of data collection, that is, who supported the research will most likely affect the outcome. 13. Be aware of "meaningless milestones" which are the underlying trend and its cause. 14. The" law of averages" is very tricky. Therefore the median is a more useful measure particularly in a wide variation of numbers. The underlying mission of Stat-Spotting is for the reader to become more mathematically astute. The frame of reference for many of the examples in the book is the health care field. As health care professionals are we rapidly accepting statistics as facts? Just a few of the book's statistical concepts that surround the health care profession are: Epidemics compare old data and ne

This is a well-written, very useful book that fills a need: How can we know whether the statistics we see in the news are genuine? This book is a field guide; it serves the same purpose as a field guide to birds, for example. It is a basic guide to recognizing questionable statistics based on the characteristics of those statistics, not any particular expertise about the subject at hand. The book first offers a handful of basic statistics about the US, such as the population, the numbers of births and deaths per year, the leading causes of deaths, etc. Then, each chapter puts those statistics-- and some basic critical thinking skills-- into various contexts. The result is a short, very readable guide to the quality of the numbers we see in the news every day, one that doesn't require much math skill. Why is this important? Because those numbers we see in the news are very often misleading, and some times they're no more than errors, blind guesses, and gross over- or understatements. If we don't have accurate data, we won't make good decisions: Garbage in, garbage out. This is a very interesting and useful book, and I recommend it highly.

One of the most important things one can do in this time of too much information is to be able to evaluate the information being presented to you. Stat-Spotting delivers by giving you tools and yardsticks to use in evaluating statistical and numerical information presented in the plethora of sources available to someone researching a topic. This is a book that I suggest everyone read...

Stat-Spotting is a basic guide to recognizing questionable statistics. The author, Joel Best, is a professor of sociology and criminal justice and the author of two previous books on the misuse of statistics. He focuses mainly on the simple statistics found in news reports, so don't expect a detailed treatment of experimental design, regression analysis, or analysis of variance. This is simply a guide to identifying numbers that don't make sense, or that are reported without enough context to make sense of,or that are presented in a way that is biased or misleading. Since his interest is mostly in sociological statistics he begins by laying out the background data that can put those numbers into their context: US population, number of births and deaths per year, leading causes of death and their frequencies, and so on. He then points out that, for the most part, extreme outcomes tend to occur less frequently than moderate outcomes. He then describes many ways in which dubious data can make its way into publication: mistakes, guesses, overly broad or narrow definitions, sample bias, etc. This is an interesting and useful book, requiring no mathematical background, and a good antidote to numerical gullibility.