| 000 | 00000cam u2200205 a 4500 | |
| 001 | 000046067061 | |
| 005 | 20210208115612 | |
| 008 | 210205s2008 caua b 001 0 eng d | |
| 010 | ▼a 2008017175 | |
| 015 | ▼a GBA893597 ▼2 bnb | |
| 020 | ▼a 9780520257467 (cloth : alk. paper) | |
| 020 | ▼a 0520257464 (cloth : alk. paper) | |
| 035 | ▼a (KERIS)REF000014941333 | |
| 040 | ▼a DLC ▼c DLC ▼d DLC ▼d 211009 | |
| 050 | 0 0 | ▼a HM535 ▼b .B477 2008 |
| 082 | 0 0 | ▼a 301.072/7 ▼2 23 |
| 084 | ▼a 301.0727 ▼2 DDCK | |
| 090 | ▼a 301.0727 ▼b B561s | |
| 100 | 1 | ▼a Best, Joel. |
| 245 | 1 0 | ▼a Stat-spotting : ▼b a field guide to identifying dubious data / ▼c Joel Best. |
| 260 | ▼a Berkeley : ▼b University of California Press, ▼c c2008. | |
| 300 | ▼a 132 p. : ▼b ill. ; ▼c 22 cm. | |
| 504 | ▼a Includes bibliographical references (p. 119-129) and index. | |
| 505 | 0 | ▼a Getting started -- Spotting questionable numbers -- Background -- Statistical benchmarks -- Severity and frequency -- Varieties of dubious data -- Blunders -- The slippery decimal point -- Botched translations -- Misleading graphs -- Careless calculations -- Sources: who counted--and why? -- Big round numbers -- Hyperbole -- Shocking claims -- Naming the problem -- Definitions: what did they count? -- Broad definitions -- Expanding definitions -- Changing definitions -- The uncounted -- Measurements: how did they count? -- Creating measures -- Odd units of analysis -- Loaded questions -- Raising the bar -- Technical measures -- Packaging: what are they telling us? -- Impressive formats -- Misleading samples -- Convenient time frames -- Peculiar percentages -- Selective comparisons -- Statistical milestones -- Averages -- Epidemics -- Correlations -- Discoveries -- Debates: what if they disagree? -- Causality debates -- Equality debates -- Policy debates -- Stat-spotting on your own -- Summary: common signs of dubious data -- Better data: some characteristics -- Afterword: if you had no idea things were that bad, they probably aren't -- Suggestions for those who want to continue stat-spotting. |
| 650 | 0 | ▼a Sociology ▼x Statistical methods. |
| 650 | 0 | ▼a Social problems ▼x Statistical methods. |
| 945 | ▼a KLPA |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 중앙도서관/서고6층/ | 청구기호 301.0727 B561s | 등록번호 511045591 | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
Are four million women really battered to death by their husbands or boyfriends each year? Does a young person commit suicide every thirteen minutes in the United States? Is methamphetamine our number one drug problem today? Alarming statistics bombard our daily lives, appearing in the news, on the Web, seemingly everywhere. But all too often, even the most respected publications present numbers that are miscalculated, misinterpreted, hyped, or simply misleading. Following on the heels of his highly acclaimed Damned Lies and Statistics and More Damned Lies and Statistics, Joel Best now offers this practical field guide to help everyone identify questionable statistics. Entertaining, informative, and concise, Stat-Spotting is essential reading for people who want to be more savvy and critical consumers of news and information.
Stat-Spotting features:
* Pertinent examples from today's news, including the number of deaths reported in Iraq, the threat of secondhand smoke, the increase in the number of overweight Americans, and many more
* A commonsense approach that doesn't require advanced math or statistics
Stat-Spotting features:
* Pertinent examples from today's news, including the number of deaths reported in Iraq, the threat of secondhand smoke, the increase in the number of overweight Americans, and many more
* A commonsense approach that doesn't require advanced math or statistics
정보제공 :
저자소개
조엘 베스트(지은이)
미국 버클리 대학에서 철학 박사학위를 취득했다. 캘리포니아 주립대학과 남일리노이 대학에서 강의했으며, 중서부 지역 사회학회장을 역임했다. 2006년 현재 델라웨어대학에서 사회학, 형사사법학부 교수로 재직 중이다. 지은 책으로 <Random Violence>, <Damned Lies and Statistics> 등이 있다.
목차
PART 1 GETTING STARTED A. Spotting Questionable Numbers B. Background B.1 Statistical Benchmarks B.2 Severity and Frequency PART 2 VARIETIES OF DUBIOUS DATA C. Blunders C.1 The Slippery Decimal Point C.2 Botched Translations C.3 Misleading Graphs C.4 Careless Calculations D. Sources: Who Counted-and Why? D.1 Big, Round Numbers D.2 Hyperbole D.3 Shocking Claims D.4 Naming the Problem E. Definitions: What Did They Count? E.1 Broad Definitions E.2 Expanding Definitions E.3 Changing Definitions E.4 The Uncounted F. Measurements: How Did They Count? F.1 Creating Measures F.2 Odd Units of Analysis F.3. Loaded Questions F.4 Raising the Bar F.5 Technical Measures G. Packaging: What Are They Telling Us? G.1 Impressive Formats G.2 Misleading Samples G.3 Convenient Time Frames G.4 Peculiar Percentages G.5 Selective Comparisons G.6 Statistical Milestones G.7. Averages G.8 Epidemics G.9 Correlations G.10 Discoveries H. Debates: What If They Disagree? H.1 Causality Debates H.2 Equality Debates H.3 Policy Debates PART 3 STAT-SPOTTING ON YOUR OWN I. Summary: Common Signs of Dubious Data J. Better Data: Some Characteristics K. Afterword: If You Had No Idea Things Were That Bad, They Probably Aren''t L. Suggestions for Those Who Want to Continue Stat-Spotting Acknowledgments Notes