Judgements

Question 1

Kidney cancer in USA

Answer 1

Low prevalence countries were:
rural, sparsely population, republican, Midwest, south, west

but high prevalence countries were the same

Question 2

Why do low prevalence and high prevalence countries have the same rural lifestyles?

Answer 2

Sparsely population - small sample, more variability

if done a year later, same pattern found but the particular countries with high and low prevalence rates would differ

Question 3

The Gates foundation

Answer 3

Invested 1.7 billion in a programme to create small schools, because they found a large proportion of small schools getting better results than average, didn’t look at size of worst performing schools, would’ve found the they do worse than average

Small sample = more variability found

Question 4

The law of small numbers

Answer 4

People take small samples to provide accurate estimates just as large samples do (the law of large numbers) but small samples yield extreme results more often than small samples do

Question 5

What are the misperceptions of randomness?

Answer 5

People are misled by randomness
bomb strikes on London, falling in some areas but not others, thought certain areas were deliberately missed - but this isn’t true, no evidence it wasn’t random

do basketball players get a hot hand which causes them to score baskets from several shots in a row? the sequence of them scoring loads in a row was just random

Question 6

How to we make judgements?

Answer 6

We use heuristic rules, easily applied, gives us quick answers, correct most the time but can lead us astray

making proper judgements takes a while. e.g. insurance company - know young people have more accidents so insurance should be more, but need to research this

Question 7

What are the three heuristics?

Answer 7

Anchoring and adjustment
Availability
Representativeness

Question 8

What is anchoring and adjustment?

Answer 8

When making a judgement about how likely something is, you have a starting point (the anchor) and then have to adjust this to be more accurate

Question 9

Example of anchoring and adjustment

Answer 9

A wheel of fortune stops at either 10 or 65, people asked to read result and decide if proportion of nations in the UN that are African is larger or smaller than that number, have to make a numerical estimate

people move away from initial suggestions
if landed on 10, move to 25%
if landed on 65, move to 45%
actual figure 30%, adjustment not as much as it should be

Question 10

What are 65 and 10 called in the wheel of fortune?

Answer 10

Anchors - people do not adjust enough from poor or arbitrary anchors

Question 11

What can anchors be used in?

Answer 11

Negotiations - prices for used cars

Question 12

Is adjustment used by system 1 or 2?

Answer 12

It can occur as a deliberate process but it is also subject to unconscious influences (shaking heads leads to moving further away from anchor, nodding head to less)

also evidence of a priming (system 1) component to anchoring via evoked images

Question 13

A real world example of anchoring and adjustment

Answer 13

Estate agents valuing real properties with high and low anchors showed an anchoring effect of 40%

Question 14

How do you work out the anchoring effect?

Answer 14

It is the difference between the final estimates divided by the difference between the anchors

Question 15

What are availability and representativeness based on?

Answer 15

Fundamental cognitive processes
availability - retrieval from memory
representativeness - judgements made from similarity

Question 16

What is availability?

Answer 16

It is used whenever people estimate frequency or probability by the ease of which instances or associations could be brought to mind

Question 17

Examples in which availability misleads

Answer 17

Do more English words have R as first letter or third letter? think more have r as first letter because we think of words due to the letter they begin with, but more have it as third letter

famous names experiment - when asked to remember if they were famous or not, remember more as being famous because they come to mind more easily

Question 18

Availability - domestics

Answer 18

Estimating members of couples of their relative contribution to household chores
results usually add to over 100%, your own contributions are more readily accessed from your memory than partners
people overestimate their contribution to causing arguments

Question 19

Are more examples harder to recall?

Answer 19

List 6 or 12 instances of situations in which you have been assertive and rate how assertive you are

asking for 12 led to lower ratings, because the last instances were difficult to bring to mind

background music can make things harder to remember

Question 20

Cause of death study

Answer 20

Effects of media coverage
deaths by accident overestimated compared with deaths by stroke
death by lightening underestimated compared with death by botulism - as this is in news more

Question 21

What is representativeness?

Answer 21

Making a judgement based on whether something depending on how well it fits with a stereotype or prototype - based on similarity of what you think something is like

Question 22

Tom W experiments

Answer 22

Rank base rates for graduate students in 9 areas of study
rank each area for how well a description of tom W fits typical graduate student in that area
rank areas for how likely tom w is now a graduate student in that area

People know there weren’t many computer scientist graduates in the past, but the description fits this so people thought it was a computer scientist

Question 23

Going against representativeness

Answer 23

Billy Beane - manager of basketball team, overruled his scouts who were suggesting hiring players who seemed to fit the physical stereotype or good basketball players - look at play statistics instead

he formed a very successful team for a very low cost

Question 24

What are the two problems of using representativeness?

Answer 24

Ignoring base rates (computer scientist graduates were relatively rare when the exp were carried out)

Using poor or useless information - the information in description of Tom is described as old, based on psychological tests of uncertainty

Question 25

Representativeness and the conduction fallacy

Answer 25

Given a description of Linda and then asked about what she is:

less likely to be both but still pick it - ignoring base rate

most choose second option, but the prob of being both is less likely than being one

more likely to be a bank teller with a feminist movement because she fits the stereotype, people choose this because option 2 represents the description of her more, even if it statistically less likely

the description of Linda Is crucial in producing the judgement

Question 26

Is less more?

Answer 26

Value of two dinner sets, one contains all the items in the first, a few more good items and a few broken
in a direct comparison, the set with more items is valued higher
but when the values are given by different people, the set with fewer itemises valued higher

Question 27

Probability of a cab involved in accident being blue or green

Answer 27

A cab was involved in hit and run, a blue and green company run in the city. 85% in city are green and 15% are blue, the witness identified the cab as blue. the court tested reliability of the witness and conclude that the witness correctly identified one of the two colours 80% of time and failed 20% of time.

people believe it was 80% likely to be blue, but it was actually 41%

Question 28

Cab story - if the problem is changed to a causal story

Answer 28

85% of accidents are caused by drivers of green cabs, performance then improves considerably

simple base rates are hard to engage with and use, causal base rates are easily incorporated into a story

Question 29

Experience with flying instructions

Answer 29

Praise for a well executed manoeuvre was often followed by poorer performance next time
Criticism for a poorly executed manoeuvre was followed by better performance

praise is ineffective and criticism is effective

Question 30

Double marking

Answer 30

sometimes marking is overestimated/undermarked
for 2 examiners, the best prediction is they will give it the same mark
if mark of first examiner is known, best prediction is that second examiners mark will be closer to overall mean
if mark of second examiner known, best prediction is that first examiners mark will be closer to the overall mean - other things equal, spread of marks will be the same

Question 31

What is regression to the mean?

Answer 31

Moving towards the mean, we have poor intuitions about phenomenon so don’t recognise it in the real world

Question 32

Restaurant example of regression to the mean

Answer 32

A really excellent meal at a restaurant on one visit is likely to be followed by a slightly disappointing one on the next visit