SPRING Reasoning and judgement Flashcards

1
Q

describe the sally clarke problem

A

both babies die from sudden infant death - very unlikely
argued that prob so low, more likely to be murder
but lawyers used poor statistical reasoning - two events were not independent of eachother and the prob of two independent events not the same as two linked ones (prosecutors fallacy)
ignores probabilities of two murders in same family

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

contraceptive probabiities

A

98% vs 99% effective

over time, 98% sig less effective than 99%

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

how do we make subjective probability judgements

A

decision making based on SEU

heuristic strategies to judge the likelihood of event occurance

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

describe the availability heuristic

A

judge probability based on ease comes to mind

if more freq then more available

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

tversky and kahenman availability heuristic

A

more common: r as first or third letter

most say first - easier to think of words beginning with r

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

slovic, fichoff and litchenstien 1979 availability heurostic

A

estimate freq of causes of death

overestimate those unlikely but heavily reported ie murder and underestimate those rarely reported ie emphysema

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

describe the representativeness heuristic

A

degree of correspondence between an instance and a category
ie looks and sounds like it so probably is
how much a certain set of circumstances will drive the likelihood of something based on past experience - link closely to stereotypes

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

tversky and kahenmen 1983 conjunction fallacy (representativeness heuristic)

A

liinda - bank teller, feminist bank teller, feminist
often chose feminist bank teller but statistically the least likely
two independent events make conjunction of two together even less probable - feminist bankteller is smaller subset of banktellers

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

describe anchoring heuristic

A

base estimated probability on the first piece of information we are given

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

tversky and kahenman 1974 anchoring

A
estimates under time pressure 
1. 1x2x3x4x5x6x7...
2. 7x6x5x4x3x2x1
= ~40000 
BUT estimate 1 lower than 2 based on first numbers
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

lichtenstein et al 1978 anchoring

A

achoring on first given value means fail to adjust appropriately
estimate freq of 40 causes of death
a. 50000 motor cycle or b.10000 electrocution
estimate higher for a

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

real world examples of anchoring

A

anchoring consumers for price reference so view as less expensive
credit card minimums
high prices as ref point for lower

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

what is bayesian reasoning

A

update probabilities based on additional information that causes to ignore the original base rate and focus on hit rate
make probabalistic inferences based on additional info and therefore forget the initial info given

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

kahenman and tversky 1973 base rate fallacy

A
30 engineers and 70 lawyers
stereotypical description of engineer/lawyer or neutral
neurtral tend to report 50%
rate increase if stereotypical
BASE RATE ALWAYS THE SAME: 42%
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

medical diagnosis problem base rate fallacy

casscells et al 1978

A

prob breast cancer 1%…given values of pos mamog if have cancer = 80% and not = 9.6%
= overall 7.8% prob
clinicians probability = 70-80% as ignore probability of 1% and focus on hit rate

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

gigerenzer, hofferage and ebert 1998 bayesian reasoning

A

positive HIV test - prob that ACUTALLY positive?

clinicians say 100% even if low risk patient

17
Q

gigerenzer and hofferage 1995 format of decision

A

if way info is presented is more reflective of the way we make decisions in real life then more likely to interpret in correct way
ie breast cancer problem in frequency format and most people get correct

18
Q

cosmides and tooby 1996frequentists hypothesis

why are frequency problems easier

A

inductive readoning = prob in the form of freq

easier to break down if in freuqncy form - tree

19
Q

girotto and gonzalez 2001 defective frequencies

A

if not given enough info to complete freq tree OR the information given is all drawn from multiple samples

20
Q

how might you improve bayesian reasoning problems

A

change structure of the info not the format

apply cumulative reasoning

21
Q

what are naive probabilites

A

when people make judgements on probabilities they are not expert in - relate to the alternatives they consider
ie not given probabilties then assume equiprobable

22
Q

girotto and gonzalez 2001 prob and freq use

A

prob not alwats bad ie can format as chance to improve interpretation
freq not alwats good - come harder than probabilities, dependent on the info structure

23
Q

mccloy byrne and johnson laurd 2010 improveing bayesian readoning

A

naive individuals dont use binomal formula to calc cumulative prob and instead use conjunctions ie if not yet occred then the prob = 0/no. trials
those who recog that freq cumulates over time may still underestimate the rate of increased risk
improve by presenting problems in specific ways - ie more structured layout

24
Q

johnson laird et al

sentanial resoning

A

when an individual understands discours and imagines a state of affairs then constructs mental models of the relevant situation
prepresents the possibilities and diff ways they might occur - represents explicitly what is true but not what is false
each model therefore represents an equiprobable alt unless info given to hold belief of the contrary
the greater the no of mental models a task elicits and the greater their complexity the worse the performance

25
Q

equiprobable sentenial reasoning example

A

box with red or black marble or both
prob of black marble with another marble?
- tend to say 2/3 as no info given about prob - ill specified

26
Q

define prosecutors fallacy

A

tendency to ignore that to occurances are not statistically independent from one another and therefore the probability of the two occuring is lower (more likely) than if they were independent

27
Q

how did sally clarkes case fail to prosecutors fallacy

A

argued deaths of two newborns statistically independent = 1/72m and that this equates to the prob that guilty
BUT NOT - sig lower prob because share same environment

28
Q

define bayesian reasoning

A

a means of quantifying uncertainty
rule for refining an hypothesis by factoring in additional evidence and background information, and leads to a number representing the degree of probability that the hypothesis is true
- ie accounting for independence of events in prosecutors fallacy

29
Q

define subjective probability

A

the extent to which a coherent person believes that a statement is true based on the info that is available at that time
use of heuristics to form a subjective judgement

30
Q

seidelmeier hertwig and gigerenzer 1998

availability heiristic

A

memory often good at stoing freq info
tend to measure ease info comes to mind based on no instances retrieved in given time frame and speed of retrieval
BUT - some predictions where overestimate low freq and underestimate high so not always completely accurate

31
Q

describe linda

t+k conjunction fallacy

A

31 y/o outspoken and bright
major in philosophy
concerned with discrimination and social justice
participates in antinuclear demonstrations

32
Q

diff subective prob judgements

A

availability heuristic
representativeness heuristic
anchoring

33
Q

bayesian reasoning prob

A

base rate fallacy
medical diagnosis prob
hiv screening
prob vs freq vs chance