Overview of How To Lie With Statistics by Darrell Huff

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Overview of How To Lie With Statistics by Darrell
Huff
With additional insights
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Chapter 1 - Sampling Biases

• Response Bias Tendency for people to over- or
  under-state the truth
• Non-response People who complete surveys are
  systematically different from those who fail to
  respond. Accessibility/Pride.
• Representative Sample One where all sources of
  bias have been removed. (Literary Digest)
• Questionnaire wording/Interviewer effects
• Recall Bias Tendency for one group to remember
  prior exposure in retrospective studies

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Chapter 2 - Well-Chosen Average

• Arithmetic Mean Evenly distributes the total
  among individuals. Can be unrepresentative when
  measurements are highly skewed right. (e.g. per
  capita income)
• Median Value dividing distribution into two
  equal parts. 50th percentile. (e.g. median
  household income)
• Mode Most frequently observed outcome (rarely
  reported with numeric data)

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Chapter 3 - Little Figures Not There

• Small samples Estimators with large standard
  errors, can provide seemingly very strong effects
• Low incidence rates Need very large samples for
  meaningful estimates of low frequency events
• Significance levels/margins of error Measures of
  the strength and precision of inference
• Ranges Report ranges or standard deviations
  along with means (e.g. normal ranges)
• Inferring among individuals versus populations
• Clearly label chart axes

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Chapter 4 - Much Ado About Nothing

• Probable Error Estimation error with probability
  0.5. If estimator is approximately normal, PE is
  approximately 0.675 standard errors. (Old school)
• Margin of Error Estimation error with
  probability 0.95. If estimator is approximately
  normal, PE is approximately 2 standard errors
• Clinical (practical) significance In very large
  samples an effect may be significant
  statistically, but not in a practical sense.
  Report confidence intervals as well as P-values.

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Chapter 6 - Eye-Catching Graphs

• Choice of ranges on graphs can have huge impact
  on interpretation (e.g. percent change)
• Choice of proportion of y-axis to x-axis can
  distort as well (very easy to do with modern
  software)
• Can also distort bar charts by having them start
  at positive values and/or trimming below an
  artificial baseline to 0

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Chapter 6 - 1-D Pictures

• Bar Charts and Pictorial Graphs should have areas
  proportional to values (only make comparisons in
  one dimension)

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Chapter 7 - Semiattached Figure

• Target Population Group we want to make
  inference regarding
• Study Population Group or items that experiment
  or survey is conducted on
• When comparative studies are conducted among
  products,treatments, or groups what is the
  comparison product, treatment, or group?
• Control for all other potential risk factors when
  studying effects of factors

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Chapter 8 - Causal Relationships

• Correlation does not imply causation
• Elements of causal relationships
• Association between Y and X
• Clear time ordering (X precedes Y)
• Removal of alternative explanations (controlling
  for other factors)
• Dose-Response (when possible)