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Newsroom math

Newsroom math. Prof. Steve Doig Cronkite School, ASU. Journalists hate math. Definition of journalist: A do-gooder who hates math. “Word person, not a numbers person.” 1936 JQ article noting habitual numerical errors in newspapers

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Newsroom math

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  1. Newsroom math Prof. Steve Doig Cronkite School, ASU

  2. Journalists hate math • Definition of journalist: A do-gooder who hates math. • “Word person, not a numbers person.” • 1936 JQ article noting habitual numerical errors in newspapers • Japanese 6th graders more accurate on math test than applicants to Columbia’s Graduate School of Journalism • 20% of journalists got more than half wrong on 25-question “math competency test” (Maier) • 18% of 5,100 stories examined by Phil Meyer had math errors

  3. Bad examples abound • Paulos: 300% decrease in murders • Detroit Free Press (2006): Compared ACS to Census data to get false drop in median income • KC Star (2000): Priests dying of AIDS at 4 times the rate of all Americans • Delaware ZIP Code of infant death • NYT: 51% of women without spouses

  4. Common problems • Numbers that don’t add up • Making the reader do the math • Failure to ask “Does this make sense?” • Over-precision • Ignoring sampling error margins • Implying that correlation equals causation

  5. Dangers of journalistic innumeracy • Misleads math-challenged readers/viewers • Hurts credibility among math-capable readers/viewers • Leads to charges of bias, even when cause is ignorance • Makes reporters vulnerable to being used for the agendas of others

  6. The bad news… • To be a good journalist, you MUST be able to do math

  7. The good news! • …it’s grade school math! • None of this stuff: • Calculus • Geometry proofs • Base-12 • Venn diagrams • Ballistics • Etc….

  8. My office bookcase

  9. Sarah Cohen’s tips • Keep the digits in a paragraph below 8 • Memorize common numbers on your beat • Round off – a lot • Learn to think in ratios • Envision your dream number, then calculate it

  10. Newsroom math crib sheet

  11. Comparing numbers • Difference • Percent • Percent difference • Percentage change • Millage • Per capita

  12. Difference • Okay, I won’t insult you….

  13. Percents • To get X% of Y: • Turn X% into a decimal, then multiply by Y • 20% of 90 = 0.2 * 90 = 18 • 130.5% of 45 = 1.305 * 45 = 58.7

  14. Comparing X and Y • X is what percent of Y? • X is X/Y of Y • Then multiply X/Y by 100 • 5 and 8: • 5 is 5/8 of 8 • 5 is .625 of 8 (or 62.5%) • 8 and 5 • 8 is 8/5 of 5 • 8 is 1.60 of 5 (or 160%)

  15. Comparing NEW and OLD • Percentage change! • (NEW/OLD – 1) • Or: (new – old)/old • €8 million this year, €5 million last year • (8/5 – 1) = 1.6 – 1 = 0.6 = 60%, • So the budget has increased 60% • €5 million this year, €8 million last year • (5/8 – 1) = 0.625 – 1 = - 0.375 = -37.5% • So the budget has decreased 37.5%

  16. Remember PEMDAS! • Order of algebraic operations: • Parentheses • Exponents • Multiplication • Division • Addition • Subtraction

  17. Compare X and Y (% difference) • X is (X/Y – 1) MORE/LESS than Y • Use MORE THAN if the answer is positive • Use LESS THAN if the answer is negative • 8 & 5: 8/5 –1 = 1.6 – 1 = 0.6 = 60%, so 8 is 60% more than 5 • 5 & 8: 5/8 –1 = .625 – 1 = -0.375 = -37.5%, so 5 is 37.5% less than 8

  18. Beware of base changes • Newsroom budget of €1 million grows by 10% one year to €1.1 million! • Next year, recession, so boss has to cut 10% from budget • Result: €1.1 million – 10% of €1.1 million = €990,000

  19. Beware of small bases • Easy to get big percentage change when you start with small values • Population 2000: 1,000 • Population 2010: 1,500 • Percentage change: +50% • Population 2000: 1,000,000 • Population 2010: 1,100,000 • Percentage change: +10%

  20. Property tax millage • Mill = €0.001 = 1/10th of a cent per €1 • Change “mills per dollar valuation” into “euros per €1,000 valuation” • Calculate tax based on “typical” value, like a €100,000 home • Example: Tax rate of 8 mills • €8 per €1,000, or tax of €800

  21. Rates • Number of events per some standard unit (per capita, per 100,000, etc.) • Crime rates, accident rates, etc. • RATE = (EVENTS / POPULATION ) * (“PER” Unit) • Use to compare places of different size

  22. Calculating rates • RATE = (EVENTS / POPULATION ) * (“PER” Unit) • If there were 320 murders in a population of 1,937,086, what is the murder rate per 100,000 • 320 / 1937086 = 0.0001652… • 0.0001652 * 100000 = 16.5 murders per 100,000 population

  23. Consumer Price Index • Used to correct for inflation • Get the CPI at http://www.ine.pt Price Now=CPI Now Price Then CPI Then

  24. Using the CPI • CPI in 2010 = 109 (base year 2005) • CPI in 1990 was 52,9 • Gasoline in 1990 was €0,68 per liter • X / 0,68€ = 109 / 52,9 • X = (109 / 52,9) * 0,68€ • X = 2,06 * 0,68€ = 1,40€ • Gas in 1990 cost the equivalent of €1,40 per liter in 2010 euros

  25. Newsroom Statistics • Mean (Average): Add the values, then divide by number of values • Median: Sort the values, then find the middle one • Mode (rarely used): The most common value

  26. American baseball salaries in 1994 strike year • Mean (average): $1.2 million • Median: $350,000 • Mode: $100,000

  27. Weighted average • Don’t average averages • Example: • Teacher average: €27 000 • Janitor average: €15 000 • Principal average: €65 000 • Simple average: €35 667

  28. Weighted average (continued) • Teachers: 10 000 x €27 000 = €270,0m • Janitors: 2 000 x €15 000 = € 30,0m • Principals: 500 x €65 000 = € 32,5m • Sum: 12 500 people €332,5 million • Weighted average: €26 600 (not €35 667)

  29. Public opinion surveys • Census vs. survey • A random sample is necessary • Size of the population being sampled doesn’t matter, only sample size matters

  30. Sampling error • Rule: The bigger the sample, the smaller the error • Sampling error = 1/N • N=100 1 / 100 = 1/10 = +/-10 pts. • N=400 1 / 400 = 1/20 = +/- 5 pts. • N=900 1 / 900 = 1/30 = +/- 3.3 pts • Other sources of error

  31. Estimating crowds • Beware the “official” estimate • Better method: • Estimate the area in sq meters (L x W) • 1 person/meter in a loose crowd • Divide by 0,75 for a tighter crowd • Account for turnover?

  32. Newsroom math bibliography • “Numbers in the Newsroom”, by Sarah Cohen, IRE • “Precision Journalism (4th edition)”, by Phil Meyer • “Innumeracy”, by John Allen Paulos • “A Mathematician Reads the Newspaper,” by John Allen Paulos

  33. Preguntas??

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