Calculated Risks: How to Know When Numbers Deceive YouSimon and Schuster, 2002 - 310 עמודים At the beginning of the twentieth century, H. G. Wells predicted that statistical thinking would be as necessary for citizenship in a technological world as the ability to read and write. But in the twenty-first century, we are often overwhelmed by a baffling array of percentages and probabilities as we try to navigate in a world dominated by statistics. Cognitive scientist Gerd Gigerenzer says that because we haven't learned statistical thinking, we don't understand risk and uncertainty. In order to assess risk -- everything from the risk of an automobile accident to the certainty or uncertainty of some common medical screening tests -- we need a basic understanding of statistics. Astonishingly, doctors and lawyers don't understand risk any better than anyone else. Gigerenzer reports a study in which doctors were told the results of breast cancer screenings and then were asked to explain the risks of contracting breast cancer to a woman who received a positive result from a screening. The actual risk was small because the test gives many false positives. But nearly every physician in the study overstated the risk. Yet many people will have to make important health decisions based on such information and the interpretation of that information by their doctors. Gigerenzer explains that a major obstacle to our understanding of numbers is that we live with an illusion of certainty. Many of us believe that HIV tests, DNA fingerprinting, and the growing number of genetic tests are absolutely certain. But even DNA evidence can produce spurious matches. We cling to our illusion of certainty because the medical industry, insurance companies, investment advisers, and election campaigns have become purveyors of certainty, marketing it like a commodity. To avoid confusion, says Gigerenzer, we should rely on more understandable representations of risk, such as absolute risks. For example, it is said that a mammography screening reduces the risk of breast cancer by 25 percent. But in absolute risks, that means that out of every 1,000 women who do not participate in screening, 4 will die; while out of 1,000 women who do, 3 will die. A 25 percent risk reduction sounds much more significant than a benefit that 1 out of 1,000 women will reap. This eye-opening book explains how we can overcome our ignorance of numbers and better understand the risks we may be taking with our money, our health, and our lives. |
תוכן
PARTI DARETOKNOW | 3 |
Breast Cancer Screening | 55 |
UnInformed Consent | 87 |
AIDS Counseling | 115 |
Wife Battering | 141 |
Experts on Trial | 151 |
DNA Fingerprinting | 161 |
Violent People | 185 |
How Innumeracy Can Be Exploited | 201 |
Fun Problems | 211 |
Teaching Clear Thinking | 229 |
GLOSSARY | 247 |
NOTES | 259 |
REFERENCES | 281 |
| 297 | |
מהדורות אחרות - הצג הכל
Calculated Risks: How to Know When Numbers Deceive You <span dir=ltr>Gerd Gigerenzer</span> תצוגה מקדימה מוגבלת - 2015 |
Calculated Risks: How to Know when Numbers Deceive You <span dir=ltr>Gerd Gigerenzer</span> אין תצוגה מקדימה זמינה - 2002 |
מונחים וביטויים נפוצים
50 percent absolute risk reduction actually American asked base rate battered Bayes's rule behavior benefits blood breast cancer breast cancer screening Chapter clinical clouded thinking color constancy colorectal cancer conditional probabilities confused costs counselors defendant Dershowitz diagnostic Dick disease DNA evidence DNA fingerprinting DNA profile door early detection errors estimate experts false negatives false positive rate false positives Figure Gigerenzer given a positive HIV test Hoffrage illusion of certainty infected inference informed consent innumeracy instance Kerlikowske Koehler leaflets mammography screening mastectomy mind tool Monty Hall Monty Hall problem murdered natural frequencies number needed participate patients percent chance physicians population positive mammogram positive test prob problem prosecutor's fallacy prostate cancer question random match probability reason reference class relative risk reduction reported sample sexual single-event probabilities Slovic source probability statistical thinking switch test positive test result tion treatment trial uncertainty virus warden woman women
