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E-grāmata: Statistics Behind the Headlines

, (Miami University, Oxford, Ohio, USA)
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How do you learn about whats 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.

Recenzijas

"We live in a time when information channels have been de-centralized, and the usual gatekeepers journalists, experts, public officials have lost much of their power to control what information we receive. There are positive consequences to this cultural shift, but also negative ones: misinformation, disinformation, and simple misunderstandings due to ignorance run rampant. This book is an antidote to that. Using a friendly and occasionally humorous voice, John Bailer and Rosemary Pennington teach us, general readers, how to become more critical consumers of the statistics we see every day in the news and in social media. What a delight." - Alberto Cairo, University of Miami

"With each chapter, the authors cover not only various aspects of real life but also offer various flavors of modern science, like the notion of data literacy, an overview of organizations responsible for data collection, reporting practices, and many more. They take readers on a path to investigate the background research and the forefront of storytelling. They motivate readers to hone their skills as data-savvy consumers of daily news. [ ...] Science searches for broad patterns that capture general truths about the world. Narratives search for connections through human experience that assign meaning and value to reality. The book by Bailer and Pennington masterfully stitched these two worlds together, helping to approach both research results and news coverage with a critical eye. [ ...] I am sure that this book is a great educational resource for those interested in many fields: statistics, journalism, ethics, education, and their interplay and practical applications." - Elena N. Naumova in Journal of Public Health Policy, January 2024

"A beautifully accessible, thought-provoking book that should be an invaluable resource for journalists who use statistics - which, these days, is all journalists. Through clear, current examples, the authors rigorously interrogate the way data are interpreted and presented, and force us to question how to do this in a way that better serves the public without feeding misinformation, hype, or hysteria. I'm sure I will be using it as a reference guide regularly, and recommending it to other reporters." - Angela Saini, Science Journalist, New York

"An excellent and really useful aspect of the book is that its jointly written by a statistician and a journalist. As the book itself makes clear, journalists and statisticians have different skills, different motivations, and different reasons for reporting their work. And there are others involved usually the headline on a media story is not written by the journalist who wrote the study, for example. If youre aiming to make sense of a media story about statistics, and the story behind the story, you need to understand how all that works. Not only can Bailer and Pennington cover both sides they both already have long experience of working across that professional divide, and understanding of how and why statisticians and journalists do what they do. The book is primarily aimed at a general audience but I think that it will also be helpful to statisticians and journalists who need to understand more about what the other side does. They would be able to skip some of the detail about their own profession, but seeing how everything fits together is important to anyone who needs to communicate effectively about numbers. There are other strengths too. Each main chapter uses the same basic structure for making sense of a statistically-based story, and readers can use the same structure as a basis for picking apart stories for themselves. And there are links to the excellent Stats + Stories podcast series, developed by Bailer, Pennington and others, that take the ideas forward." - Kevin McConway, The Open University

"Statistics Behind the Headlines makes statistics accessible for wide audiences. The book teaches foundational statistical concepts through engaging and timely stories. It would make an excellent textbook for any course that teaches statistical thinking, whether for undergraduates, journalists, or medical professionals. Each chapter allows readers to "peer under the hood" of data-based claims and equips them with the tools needed to critically evaluate these claims. The reader-friendly style provides a refreshing contrast to the stodgy and intimidating style of most statistics books. Readers will develop an appreciation for statistics; learn how to spot hype and misinformation; and become more savvy consumers of numbers-based news." - Kristin Sainani, Stanford University

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.