(2008): 740–53.

settle our differences : Barbara Mellers, Ralph Hertwig, and Daniel
Kahneman, “Do Frequency Representations Eliminate Conjunction
Effects? An Exercise in Adversarial Collaboration,” Psychological
Science 12 (2001): 269–75.

16: Causes Trump Statistics

correct answer is 41% : Applying Bayes’s rule in odds form, the prior odds
are the odds for the Blue cab from the base rate, and the likelihood ratio is
the ratio of the probability of the witness saying the cab is Blue if it is Blue,
divided by the probability of the witness saying the cab is Blue if it is
Green: posterior odds = (.15/.85) × (.80/.20) = .706. The odds are the ratio
of the probability that the cab is Blue, divided by the probability that the cab
is Green. To obtain the probability that the cab is Blue, we compute:
Probability (Blue) = .706/1. 706 = .41. The probability that the cab is Blue
is 41%.
not too far from the Bayesian : Amos Tversky and Daniel Kahneman,
“Causal Schemas in Judgments Under Uncertainty,” in Progress in Social
Psychology , ed. Morris Fishbein (Hillsdale, NJ: Erlbaum, 1980), 49–72.
University of Michigan : Richard E. Nisbett and Eugene Borgida,
“Attribution and the Psychology of Prediction,” Journal of Personality and
Social Psychology 32 (1975): 932–43.
relieved of responsibility : John M. Darley and Bibb Latane, “Bystander
Intervention in Emergencies: Diffusion of Responsibility,” Journal of
Personality and Social Psychology 8 (1968): 377–83.

17: Regression to the Mean

help of the most brilliant statisticians : Michael Bulmer, Francis Galton:
Pioneer of Heredity and Biometry (Baltimore: Johns Hopkins University
Press, 2003).
standard scores: Researchers transform each original score into a
standard score by subtracting the mean and dividing the result by the
standard deviation. Standard scores have a mean of zero and a standard
deviation of 1, can be compared across variables (especially when the
statistica {he deviatiol distributions of the original scores are similar), and
have many desirable mathematical properties, which Galton had to work
out to understand the nature of correlation and regression.
correlation between parent and child : This will not be true in an