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E-raamat: Statistics Behind the Headlines [Taylor & Francis e-raamat]

(Miami University, Oxford, Ohio, USA),
  • Formaat: 204 pages, 14 Tables, black and white; 5 Line drawings, color; 4 Line drawings, black and white; 9 Halftones, color; 2 Halftones, black and white; 14 Illustrations, color; 6 Illustrations, black and white
  • Sari: ASA-CRC Series on Statistical Reasoning in Science and Society
  • Ilmumisaeg: 28-Sep-2022
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-13: 9781003023401
Teised raamatud teemal:
  • Taylor & Francis e-raamat
  • Hind: 101,56 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
  • Tavahind: 145,08 €
  • Säästad 30%
Statistics Behind the HeadlinesSuurem pilt
  • Formaat: 204 pages, 14 Tables, black and white; 5 Line drawings, color; 4 Line drawings, black and white; 9 Halftones, color; 2 Halftones, black and white; 14 Illustrations, color; 6 Illustrations, black and white
  • Sari: ASA-CRC Series on Statistical Reasoning in Science and Society
  • Ilmumisaeg: 28-Sep-2022
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-13: 9781003023401
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9781003023401
"If you are looking to start developing your data self-defense and critical news consumption skills, this book is for you! It reflects a long-term collaboration between a statistician and a journalist to shed light on the statistics behind the stories and the stories behind the statistics. The only prerequisite for enjoying this book is an interest in developing the skills and insights for better understanding news stories that incorporate quantitative information. Chapters in Statistics Behind the Headlines kick off with a news story headline and a summary of the story itself. The meat of each chapter consists of an exploration of the statistical and journalism concepts needed to understand the data analyzed and reported in the story"--

How do you learn about what’s going on in the world? Did a news headline grab your attention? Did a news story report on recent research? What do you need to know to be a critical consumer of the news you read? If you are looking to start developing your data self-defense and critical news consumption skills, this book is for you! It reflects a long-term collaboration between a statistician and a journalist to shed light on the statistics behind the stories and the stories behind the statistics. The only prerequisite for enjoying this book is an interest in developing the skills and insights for better understanding news stories that incorporate quantitative information.

Chapters in Statistics Behind the Headlines kick off with a news story headline and a summary of the story itself. The meat of each chapter consists of an exploration of the statistical and journalism concepts needed to understand the data analyzed and reported in the story. The chapters are organized around these sections:

  • What ideas will you encounter in this chapter?
  • What is claimed? Is it appropriate?
  • Who is claiming this?
  • Why is it claimed? What makes this a story worth telling?
  • Is this a good measure of impact?
  • How is the claim supported?
  • What evidence is reported?
  • What is the quality/strength of the evidence?
  • Does the claim seem reasonable?
  • How does this claim fit with what is already known?
  • How much does this matter?
  • Considering the coverage

Chapters close with connections to the Stats + Stories podcast.



Constructed from series of headlines and articles connected to scientific or government report. Exploration of statistical concepts in report: source of data, issues with quantifying and measuring variables/features, representation and analysis of statistical models, discussion of whether headline and article fairly captured original report.
Preface xv
Acknowledgments xxiii
Chapter 1 A Field Guide to Reading the Statistics behind the Headlines
1(10)
Journalists And Statisticians Share Similar Goals
2(1)
Structure Of Each
Chapter
3(1)
Statistical Concepts To Be Explored
4(1)
What Is It? How Much Is There?
4(1)
Data Generation/Producing Data
5(1)
Describing Data
5(1)
Drawing Conclusions From Data
6(1)
If I Do This, Then That Will Happen
7(1)
Journalism 101
7(4)
Chapter 2 Predicting Global Population Growth and Framing How You Report It
11(16)
Story Summary
12(1)
What Ideas Will You Encounter In This
Chapter?
13(1)
What Is Claimed? Is It Appropriate?
13(1)
Who Is Claiming This?
14(1)
Why Is It Claimed? What Makes This A Story Worth Telling?
15(1)
Is This A Good Measure Of Impact?
15(1)
How Is The Claim Supported?
15(3)
What Evidence Is Reported?
17(1)
What Is the Quality/Strength of the Evidence?
18(1)
Does The Claim Seem Reasonable?
18(1)
How Does This Claim Fit With What Is Already Known?
19(1)
How Much Does This Matter?
19(2)
Comparison of Population Perspective versus Individual Perspective?
20(1)
Will I Change My Behavior as a Consequence of This?
20(1)
Considering The Coverage
21(3)
Review
24(1)
Stats + Stories Podcasts
24(1)
References -- World Population Projection
25(1)
Notes
25(2)
Chapter 3 Social Media and Mental Health
27(28)
Story Summary
28(1)
What Ideas Will You Encounter In This
Chapter?
29(1)
What Is Claimed? Is It Appropriate?
29(1)
Who Is Claiming This?
30(1)
Why Is It Claimed?
31(1)
Is This A Good Measure Of Impact?
31(4)
Variables
31(3)
Odds and Odds Ratios
34(1)
How Is The Claim Supported?
35(10)
What Evidence Is Reported?
38(5)
What Is the Quality/Strength of the Evidence?
43(2)
Is A 2X Increase In Odds Of Problems A Cause For Concern?
45(1)
What Are The Baseline Rates Of These Mental Health Problems?
46(1)
Is The Claim Reasonable In Itself? Does Prior Belief Impact My Belief? Confirmation Bias?
46(1)
How Does This Claim Fit With What Is Already Known?
46(1)
How Much Does This Matter To Me?
47(1)
Does A Study Of U.S. Young Teens Translate To Older Teens Or To Other Countries?
48(1)
Considering The Coverage
49(3)
Review
52(1)
Stats + Stories Podcasts
52(1)
Notes
53(2)
Chapter 4 Speedy Sneakers: Technological Boosterism or Sound Science?
55(20)
Story Summary
56(1)
What Ideas Will You Encounter In This
Chapter?
57(1)
What Is Claimed? Is It Appropriate?
57(1)
Who Is Claiming This?
57(1)
Why Is It Claimed?
58(2)
Is This A Good Measure Of Impact?
60(1)
How Is The Claim Supported?
60(6)
What Evidence Is Reported?
60(3)
What Is the Quality/Strength of the Evidence?
63(3)
Is The Claim Reasonable In Itself? Does Prior Belief Impact My Belief? Confirmation Bias?
66(1)
How Does This Claim Fit With What Is Already Known?
66(1)
How Much Does This Matter To Me?
67(1)
Considering The Coverage
68(3)
Review
71(1)
To Learn More
72(1)
A Bonus Story
72(1)
Stats + Stories Podcasts
73(2)
Chapter 5 Investigating Series Binge-Watching
75(16)
Story Summary
77(1)
What Ideas Will You Encounter In This
Chapter?
77(1)
What Is Claimed? Is It Appropriate?
77(1)
Who Is Claiming This?
78(1)
Why Is It Claimed?
78(1)
Is This A Good Measure Of Impact?
79(1)
How Is The Claim Supported?
79(1)
What Evidence Is Reported?
80(1)
How Much Television Do You Watch? Government Survey Says
80(2)
Are You A Binge-Watcher? Industry Report Says
82(2)
Is Watching Lots Of Tv Is Good, Bad Or Both For You? Experts Say
84(1)
Binging And Stress? Scientific Presentation Says
84(2)
What Is The Quality/Strength Of The Evidence?
86(1)
Is The Claim Reasonable In Itself? Does Prior Belief Impact My Belief? Confirmation Bias?
86(1)
How Does This Claim Fit With What Is Already Known?
87(1)
How Much Does This Matter To Me?
87(1)
Considering The Coverage
87(2)
Review
89(1)
Stats + Stories Podcasts
90(1)
Chapter 6 Tracking the Spread of "False News"
91(16)
Story Summary
92(1)
What Ideas Will You Encounter In This
Chapter?
93(1)
What Is Claimed? And Is It Appropriate?
93(1)
Who Is Claiming This?
94(1)
Why Is It Claimed?
94(1)
Is This A Good Measure Of Impact?
95(2)
How Is The Claim Supported?
97(3)
What Evidence Is Reported?
97(1)
What Is the Quality/Strength of the Evidence?
98(2)
Is The Claim Reasonable In Itself? Does Prior Belief Impact My Belief? Confirmation Bias?
100(1)
How Does This Claim Fit With What Is Already Known?
100(1)
How Much Does This Matter To Me?
101(1)
Considering The Coverage
101(4)
Review
105(1)
Stats + Stories Podcasts
105(1)
Note
106(1)
Chapter 7 Modeling What It Means to "Flatten the Curve"
107(16)
Story Summary
108(1)
What Ideas Will You Encounter In This
Chapter?
109(1)
What Is Claimed? Is It Appropriate?
109(1)
Who Is Claiming This?
110(1)
Why Is It Claimed?
110(1)
Is This A Good Measure Of Impact?
111(1)
How Is The Claim Supported?
111(4)
What Evidence Is Reported?
112(1)
What Is the Quality/Strength of the Evidence?
113(2)
Is The Claim Reasonable In Itself? Does Prior Belief Impact My Belief? Confirmation Bias?
115(1)
How Does This Claim Fit With What Is Already Known?
115(1)
How Much Does This Matter To Me?
116(1)
Considering The Coverage
116(3)
Review And Recap
119(1)
Covidcoda
119(1)
Stats + Stories Podcasts
120(3)
Chapter 8 One Governor, Two Outcomes and Three COVID Tests
123(16)
Story Summary
124(1)
What Ideas Will You Encounter In This
Chapter?
125(1)
What Is Claimed? Is It Appropriate?
125(1)
Who Is Claiming This?
126(1)
Why Is It Claimed?
126(1)
Is This A Good Measure Of Impact?
126(1)
How Is The Claim Supported?
127(2)
What Evidence Is Reported?
128(1)
What Is the Quality/Strength of the Evidence?
129(1)
Is The Claim Reasonable In Itself? Does Prior Belief Impact My Belief? Confirmation Bias
129(1)
Community With Low Rate Of Infection
129(1)
Rapid, Less Accurate Test
129(1)
Slower, More Accurate Test
130(1)
Community With A Higher Rate Of Infection
130(2)
Rapid, Less Accurate Test
131(1)
Slower, More Accurate Test
131(1)
How Much Does This Matter To Me?
132(1)
Considering The Coverage
132(2)
Review
134(1)
Stats + Stories Podcasts
135(4)
Chapter 9 Research Reproducibility and Reporting Results
139(20)
Story Summary
140(2)
What Ideas Will You Encounter In This
Chapter?
142(1)
What Is Claimed? Is It Appropriate?
142(1)
Who Is Claiming This?
143(1)
Why Is It Claimed? What Makes This A Story Worth Telling?
143(1)
Is This A Good Measure Of Impact?
144(1)
How Is The Claim Supported?
144(5)
What Evidence Is Reported?
144(3)
What Is the Quality/Strength of the Evidence?
147(2)
Does The Claim Seem Reasonable?
149(1)
How Does This Claim Fit With What Is Already Known?
149(1)
How Much Does This Matter?
150(1)
Comparison of Population Perspective versus Individual Perspective?
150(1)
Will I Change My Behavior as a Consequence of This?
150(1)
Considering The Coverage
151(3)
Review
154(2)
Coda: A New 3 R'S?
156(1)
Stats + Stories Podcasts
157(2)
Chapter 10 Now, What?
159(10)
Consider The Weight Of Evidence
163(3)
Consider The Source
166(1)
Consider The History
166(1)
Be A Critical Reader Of Everything
167(2)
Bibliography 169(4)
Index 173
A. John Bailer was University Distinguished Professor and Chair in the Department of Statistics at Miami University and an affiliate member of the Departments of Biology, Media, Journalism and Film and Sociology and Gerontology. His interests include promoting quantitative literacy and enhancing connections between statistics and journalism which resulted in the awardwinning Stats + Stories podcast that he started with journalism colleagues in 2013.

Rosemary Pennington is Associate Professor in the Department of Media, Journalism and Film at Miami University. Her research examines the ways that marginalized groups are represented in media as well as how members of such groups may use media to challenge those representations. Pennington was a public broadcasting journalist working in Athens, Ohio, and Birmingham, Alabama.
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