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Evidence and Inference: Quantitative Literacy for Midcareer Journalists
Jessica S Ancker, MPH, PhD, assistant professor, Weill Cornell Medical College
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Evidence and Inference • Advanced research techniques • Gathering and assessing information • Statistical literacy • Cognitive/psychological biases • Rigorous interviewing techniques • Understanding the work of experts • Locating material in historical archives and
databases • Testing assumptions and hypotheses • Recognizing ways stories can distort the truth • Making sure that reporting firmly proves its points
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Nicholas Lemann, Dean, Columbia Journalism School
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Columbia MA program
• Students: – Midcareer
– PowerPoint-naive
– Intense listeners/notetakers
– Recognize value in topic
– Uninterested in grades
– Interested in defense and offense
– Have never opened Excel
– Math-phobes
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• Measure heart rates in class, captured in spreadsheet, graphs
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Case example: Data distributions
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• Measure heart rates in class, captured in spreadsheet, graphs
• Income
average: $71,750
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Case example: Data distributions
$50,000 $66,000 $75,000
$96,000 $1,000,000
average: $257,400
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• Measure heart rates in class, captured in spreadsheet, graphs
• Income
• Malcolm Gladwell: “Million-dollar Murray”
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Case example: Data distributions
$50,000 $66,000 $75,000
$96,000 $1,000,000
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Case example: Margin of error
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Margin of error exists but is not acknowledged
Margin of error exists and is illustrated
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Case example: Margin of error
“Mr. Obama is ahead in Florida by 49 percent to 46 percent and in Wisconsin by 49 percent to 47 percent — differences within the polls’ margin of sampling error of plus or minus three percentage points.”
– Cooper and Sussman, “In poll, Obama is given trust over Medicare,” The New York Times, August 23, 2012
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Case example: Article critique
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Case example: Article critique
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The original graph (p. 42). The claim: Strontium levels were higher in children who lived within 40 miles of nuclear plants (gray bars) than
in those who lived farther away (black bars).
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1 2 3 4 5 6If we fix the y axis, the bars will be proportionate
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Sample data are merely estimates of population values. The 95% confidence intervals (“margins of error”) show ranges for the population numbers that are compatible with the sample data, given the errors
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Now we can see that in 3 of the 6 comparisons, the mean from one
bar is within the margin of error for the other one. For these comparisons, we are less sure that the means are different.
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Approach and lessons
• Keep it grounded in current events
• Goal should be for every single person to take away something useful
1. Begin with intuitive gist and examples
2. Follow with simple math
3. Recap with gist and examples
• Don’t underestimate the degree of math anxiety in this population!
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Thank you!
Jessica S Ancker, MPH, PhD
Center for Healthcare Informatics and Policy
Weill Cornell Medical College
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