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Lies and Stats > L5 Data visualisation > Flashcards

L5 Data visualisation Flashcards

1
Q

everything we measure carries a what?

A

element of uncertainty

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2
Q

how do we account for random error?

A

reporting results with a margin of error

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3
Q

what war was Florence Nightingale in?

A

Crimean

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4
Q

what did Florence Nightingale do from July 1854 to end of 1855?

A

document:
- how many soldiers died
- what month they died in
- cause of death

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5
Q

when was FN’s work published?

A

1858

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6
Q

what did FN created? and what did this do?

A

visualisations of her data to send back to London
- was effective as allowed British army to see where deaths were preventable and where to allocate resources

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7
Q

standard bar charts display -

A

categorical data clearly

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8
Q

if a bar chart is not shown as a percentage when what does it not need to do?

A

= 100

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9
Q

what are bar charts good for?

A

comparing the size of groups

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10
Q

what is the difference in a cluster bar chart?

A

each year has a cluster of bars

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11
Q

what is a cluster bar chart good at?

A

showing patterns

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12
Q

are 3D bar charts good / should we use them?

A

no

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13
Q

why are 3D bar charts so bad?

A
  • look bad
  • difficult to interpret
  • difficult to directly compare the height of bars
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14
Q

when are graphs more beneficial than tables?

A

when there is lots of information points and data
- i.e. different factors (years / places / etc.)

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15
Q

what did Hans Rosling do?

A

help develop stats to do with health
- showed Govs. where to put money and resources

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16
Q

do statistics prove things? what do they do?

A

no
- they help us understand how uncertain we should be about a measurement

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17
Q

pie charts purpose = to

A

illustrate proportions

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18
Q

pie charts show -

A

relative size of categories that add up to 100%

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19
Q

who dislikes pie charts?

A

data scientists

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20
Q

why do data scientists dislike pie charts?

A

as they take up a lot of space

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21
Q

what can sometimes just show info better than pie charts?

A

tables

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22
Q

histograms are normally used to display -

A

continuous numerical data

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23
Q

what are histograms give us quickly?

A

an overview of the spread of data

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24
Q

with histograms what are we interested in that’s different to bar charts?

A

the size of each bin - not height

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25
Q

data is right skewed when is it….

A

bigger than median

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26
Q

what are density plots similar to?

A

histograms

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27
Q

draw what a density plot looks like:

A

.

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28
Q

density plots are a what?

A

smoothed out version of a histogram

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29
Q

what are density plots as if you did?

A

drew a freehand line over a histogram

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30
Q

what does histograms show?

A

the distribution of a continuous variable

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31
Q

box plots = a

A

standardised way of displaying the distribution of data on a five number summary

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32
Q

what are box plots sometimes known as?

A

whisper plots

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33
Q

what are box plots best at telling us about?

A

outliers and what their values are

34
Q

line graphs are used to represent -

A

2 continuous variables

35
Q

different lines show…

A

different things (i.e. countries)

36
Q

what are line charts often used to illustrate?
- and example

A

financial data
- i.e. stock market performance over time

37
Q

what is the x axis normally in line charts?

A

time

38
Q

what axis is time normally in line charts?

A

x

39
Q

what can line charts also sometimes show?
- and example

A

demographic information
- i.e. how life expectancy changes over time

40
Q

scatterplots are sometimes known as -

A

scattergraphs

41
Q

scatterplots are used to show -

A

2 continuous variables

42
Q

colour in scatterplots show?

A

different things

43
Q

example of a scatterplot =

A

-GDP per capita (x axis)
-Life expectancy (y axis)

44
Q

example of use of different size plots in a scatterplot =

A

population size

45
Q

logarithmic scales also known as -

A

log scales

46
Q

what are log scales similar to?

A

scatterplots

47
Q

log scales PRO =

A

easier to differentiate than a scatterplot, as not all squeezed together

48
Q

log scales CON =

A

can be misleading + difficult to interpret

49
Q

do the media normally use log scales?

A

no

50
Q

(continuous data) Ordinal =

A

data that has natural ordering and hierarchy

51
Q

example of ordinal data =

A

satisfaction rating / level of agreement

52
Q

(continuous data) Nominal =

A

data has no natural ordering nor hierarchies

53
Q

example of nominal data =

A

gender / eye colour

54
Q

(categorical data) Discrete =

A

can take specific values + infinite options

55
Q

example of discrete data =

A

shoe size / age in years

56
Q

(categorical data) Interval =

A

infinite options, can take any value in a given interval

57
Q

example of interval data =

A

weight / percentage

58
Q

what do continuous variables measure?

A

things that vary continuously

59
Q

examples of continuous variables =

A

height / weight / income / age / mass

60
Q

continuous variables examples are most often seen in…..

A

nature

61
Q

categorical variables measure?

A

things that fall into categories

62
Q

categorical variable examples can often be seen in the….

A

social world

63
Q

true or false - continuous variables can be transformed into categorical ones

A

true

64
Q

true or false - data visualisations can mislead us (and intentionally sometimes?)

A

true

65
Q

truncating the axes =

A

shortening the height of the bars

66
Q

what does truncating the axes make it look like?

A

makes it look like there is a much larger gap than there is

67
Q

what is the problem with truncating the axes?

A

does not give a true representation of the difference between groups

68
Q

what does truncating the axes defeat the point of?

A

plotting a chart

69
Q

Beware also of d… a…..

A

dual axes

70
Q

dual axes can make things…

A

look more closely related than in reality they actually are

71
Q

dual axes can sometimes try and…

A

force relationships

72
Q

when it comes to dual axes us as the audience should always make sure to -

A

double check

73
Q

must also be aware of researchers being ………… about ……

A

selective about data

74
Q

researchers being selective about data can be -

A

misleading

75
Q

where can we often see researchers being selective about data?

A

in headlines

76
Q

x axis =

A

line on a graph that runs horizontally (left right)

77
Q

y axis =

A

line on a graph that runs vertically (up down)

78
Q

all axis’ run through what?

A

zero

79
Q

what are the most common types of data visualisation

A

bar charts
line charts
scatterplots

80
Q

…….. are an important part of data in the media

A

graphs

Lies and Stats (6 decks)

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