L1 Why numbers matter Flashcards

1
Q

Who produced the famous theory with an error in it?

A

Reinhart-Rogoff

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

what was the famous theory with an error in it?

A

the Reinhart-Rogoff theory

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

what does GDP stand for?

A

gross domestic product

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

what did they believe to be the threshold debt value of GDP where there would be a growth decline (in wealthy countries)?

A

90%

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

when are austerity measures introduced?

A

when too much debt has been taken on

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

what are the Reinhart-Rogoff theory influential in and what did it help justify?

A

politics + helped governments to make and justify difficult decisions (such as spending cuts)

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

who was it that made many cuts as of the Reinhart-Rogoff theory?

A

George Osbourne (Chancellor of Exchequer, 2010-2016)

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

we must investigate statistical claims carefully because…

A

…we may be lead astray

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

what does GDP do?

A

measures countries size and health of their economy

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

what do “when x happens, y happens” claims normally suggest?

A

causality

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

what did causality lead George Osbourne to do?

A

jump to conclusions

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

what does a high GDP mean?

A

that a country is doing well economically

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

in reality, “when x happens, y happens” claims are typically…

A

…more complicated

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

we must investigate statistical claims…

A

…carefully

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

who criticised the Reinhart-Rogoff theory?

A

Thomas Herndon

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

what did Herndon argue?

A

he disagreed with the the Reinhart-Rogoff theory - believing that there was no longer a ‘magic’ GDP threshold and that their 90% idea was phoney and didn’t exist

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

when is a country deemed to be in a recession?

A

after 2 quarters (6 months) of negative growth

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

percentage change =

A

an increase / decrease in relative terms in contrast to absolute change, expressed as a percentage

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

percentage point change =

A

difference between final and initial values

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

when dealing with stats and numbers in the news what are we are the audience?

A

responsible for interpreting them

21
Q

what was Tim Harford’s 6 step guide for dealing with stats and numbers in the new?

A

1) observe feelings
2) understand the claim (what’s being felt out / what does it really mean)
3) get the backstory
4) put it into perspective (is it a big number / is there a trend / is it significant)
5) embrace imprecision
6) be curious

22
Q

when dealing with numbers what perspective should we have?

A

think about is it a big number?

23
Q

what must we do to see if it’s a big number?

A
  • find a relative comparison
  • break it down into meaningful components
  • consider the context (put it into perspective)
  • for variables: consider the underlying distribution
  • check for outliers: beware of cherrypicking
24
Q

what is the human brain bad at?

A

imagining what very big (or very small) numbers mean and look like

25
who has the world's highest GDP (+ but what do they do?)
the US - but they spend more than they earn
26
is 90% GDP a big number (Reinhart-Rogoff theory)
depends - New Zealand = 19% Switzerland = 41%
27
example of where we are bad at understanding numbers:
2018 story where £50,000,000 worth of cocaine was seized at the airport working out to 500kg - so yes it was a big number
28
what has austerity always been presented as?
a necessity, not a choice
29
what did policy markers do when it came to austerity?
make decisions out of wants, not needs
30
is austerity being a necessity not a choice the reality of the situation?
no
31
all statistical claims have...
...elements of uncertainty
32
why do all statistical claims have elements of uncertainty?
as many are based on averages
33
the mean is a...
...one dimensional thing
34
what does the mean fail to do?
tell us about the spread of data
35
what does the mean suggest?
where the centre might be
36
the mean is sensitive to outliers - true or false?
true
37
the median is also a...
...one dimensional thing
38
what do statistician sometimes call the median?
the 'Geometric man'
39
what goes a long way when looking at numbers, data and stats?
common sense
40
we are sometimes purposefully being led astray - true or false?
true
41
we should ......... stat claims.
investigate
42
what may you sometimes do with outliers and extremes?
exclude them from analyses
43
if outliers and extremes are to be excluded from analyses, then what must happen?
the reader must be told and explained as to why - such as; if there was a major anomaly
44
austerity =
political and economic measures that aim to reduce gov. budget deficits through spending cuts / tax increases (or both)
45
how is GDP calculated?
estimated as the sum total of a nation's consumer spending, government spending, investments and trade balance
46
variable =
a number, element or feature that is not fixed, but can vary from one measurement to another
47
mean =
the arithmetic average (the sum of a set of numbers divided by how many numbers there are
48
median =
the middle number when all are ranked
49
outlier =
a particular extreme value in a dataset