16 - Chapter 16: Causes Trump Statistics

Chapter 16: Causes Trump Statistics

• Example of car accident, blue & green cabs

A cab was involved in a hit-and-run accident at night.

Two cab companies, the Green and the Blue, operate in the city.

You are given the following data:

85% of the cabs in the city are Green and 15% are Blue.

A witness identified the cab as Blue. The court tested the reliability of the witness under the circumstances that existed on the night of the accident and concluded that the witness correctly identified each one of the two colors 80% of the time and failed 20% of the time.

What is the probability that the cab involved in the accident was Blue rather than Green?

• example gives 2 items of info: base rate, unreliable witness — w/o witness, prob. is 15%

Causal Stereotypes

• now change example, base rate is same but presented diff’ly:

The two companies operate the same number of cabs, but Green cabs are involved in 85% of accidents.

The information about the witness is as in the previous version.

• the 2 examples are the same math’ly — in 2nd, people see & use base rate, changed presentation implies causal story of reckless Green drivers, stereotype — base rate vs. reliabilitiy of witness  — test subjts came close to Bayesian answ. of 41% — stereotyping improved accuracy
• statistical base rates (underweighted or ignored if ∃info abt specific case) vs causal base rates (change view of cause of indiv’l case, accepted as info abt the case, easily combined w. other info abt the case)

• when we think abt categories, we (S1) think in terms of norms, prototypical exemplars, stereotypes (for social categ’s) — for social reasons we try to avoid using stereotypes — however, neglecting valid stereotypes à poorer judg’ts

Causal Situations

• causal base rates — e.g. test w. 75% pass rate vs. test w. 25% pass rate, Did A pass? Given desc. of A — base rates imply story of v. difficult test, so subj’ts used causal base rates correctly
• noncausal base rates — changed presentation of base rates thusly, “The investigator was mainly intsted in causes of failure, and constructed a sample in wh. 75% had failed the exam” — noncausal base rate had less effect on answ’s
• but not as simple as “causal base rates are used; merely stat’l facts are (more or less) neglected”

Can Psychology be Taught?

• inferences from base rates that conflict w. other beliefs will not be accepted — teaching psych.  mostly a waste of time
• “the helping experiemtn, students hear request over headphones,  think someone is dying but few (27%) try to help if they believe ∃ others who cd help, feel relieved of responsib. — after exp’t students still not change their view of selves & others as helpful
• however, when shown 2 indiv’ls, told they did not help, were able to estimate quite well the correct base rate — saw that helping is more difficult than they thought
• “Subjects’ unwillingness to deduce the particular from the general was matched only by their willingness to infer the general from the particular.”
• can teach surprising stat’l facts about human behavior, will “learn” the facts, but does not change real views — on other hand, surprising indiv’l cases hv powerful impact, more effective for teaching psych. because the incongruity must be resolved & embedded in a causal story

Speaking of Causes and Statistics

• “We can’t assume that they will really learn anything from mere statistics. Let’s show them one or two representative individual cases to influence their System 1.”
• “No need to worry about this statistical information being ignored. On the contrary, it will immediately be used to feed a stereotype.”