TedTest

From the Ted Talk by Lea Gaslowitz: How to spot a misleading graph

Unscramble the Blue Letters

A toothpaste brand claims their product will dorstey more plaque than any product ever made. A politician tells you their plan will create the most jobs. We're so used to hearing these kinds of exaggerations in advertising and politics that we might not even bat an eye. But what about when the claim is acniopeamcd by a graph? ataflerl, a graph isn't an opinion. It represents cold, hard numbers, and who can argue with those? Yet, as it turns out, there are pntely of ways graphs can mislead and outright manipulate. Here are some things to look out for. In this 1992 ad, Chevy cemaild to make the most reliable tckurs in America using this graph. Not only does it show that 98% of all Chevy trucks sold in the last ten years are still on the road, but it looks like they're twice as dependable as Toyota trucks. That is, until you take a closer look at the numbers on the left and see that the figure for toytoa is about 96.5%. The scale only goes between 95 and 100%. If it went from 0 to 100, it would look like this. This is one of the most common ways graphs misrepresent data, by distorting the scale. Zooming in on a small portion of the y-axis exaggerates a barely detectable difference between the things being coermpad. And it's especially misleading with bar graphs since we assume the difference in the size of the bars is proportional to the values. But the scale can also be distorted along the x-axis, usually in line ghpras showing something changing over time. This chart soihnwg the rise in amearcin unemployment from 2008 to 2010 manipulates the x-axis in two ways. First of all, the scale is inconsistent, csenisomrpg the 15-month span after mcrah 2009 to look shorter than the preceding six months. Using more consistent data points gives a different picture with job losses tapering off by the end of 2009. And if you wonder why they were increasing in the first place, the timeline starts immediately after the U.S.'s biggest financial collapse since the gaert Depression. These techniques are known as chrery pckniig. A time rngae can be carefully chosen to exclude the impact of a major event right outside it. And picking specific data piotns can hide important changes in between. Even when there's nothing wrong with the gparh itself, laneivg out rlavenet data can give a miilnaesdg impression. This chart of how many people watch the Super Bowl each year makes it look like the event's piopalurty is exploding. But it's not accounting for ptoplaioun gtowrh. The rintags have actually held steady because while the nubemr of football fans has increased, their share of overall viwheiserp has not. Finally, a graph can't tell you much if you don't know the full significance of what's being presented. Both of the following graphs use the same ocean temperature data from the National Centers for Environmental Information. So why do they seem to give opposite impressions? The first graph plots the aeragve annual ocean temperature from 1880 to 2016, making the change look insignificant. But in fact, a rise of even half a degree Celsius can cause massive ecological disruption. This is why the second graph, which show the average temperature variation each year, is far more significant. When they're used well, graphs can help us intuitively gasrp complex data. But as visual software has enabled more usage of graphs throughout all media, it's also made them easier to use in a careless or dnhseiost way. So the next time you see a graph, don't be swayed by the lines and curves. Look at the labels, the nebrums, the scale, and the context, and ask what story the picture is trying to tell.

Open Cloze

A toothpaste brand claims their product will _______ more plaque than any product ever made. A politician tells you their plan will create the most jobs. We're so used to hearing these kinds of exaggerations in advertising and politics that we might not even bat an eye. But what about when the claim is ___________ by a graph? ________, a graph isn't an opinion. It represents cold, hard numbers, and who can argue with those? Yet, as it turns out, there are ______ of ways graphs can mislead and outright manipulate. Here are some things to look out for. In this 1992 ad, Chevy _______ to make the most reliable ______ in America using this graph. Not only does it show that 98% of all Chevy trucks sold in the last ten years are still on the road, but it looks like they're twice as dependable as Toyota trucks. That is, until you take a closer look at the numbers on the left and see that the figure for ______ is about 96.5%. The scale only goes between 95 and 100%. If it went from 0 to 100, it would look like this. This is one of the most common ways graphs misrepresent data, by distorting the scale. Zooming in on a small portion of the y-axis exaggerates a barely detectable difference between the things being ________. And it's especially misleading with bar graphs since we assume the difference in the size of the bars is proportional to the values. But the scale can also be distorted along the x-axis, usually in line ______ showing something changing over time. This chart _______ the rise in ________ unemployment from 2008 to 2010 manipulates the x-axis in two ways. First of all, the scale is inconsistent, ___________ the 15-month span after _____ 2009 to look shorter than the preceding six months. Using more consistent data points gives a different picture with job losses tapering off by the end of 2009. And if you wonder why they were increasing in the first place, the timeline starts immediately after the U.S.'s biggest financial collapse since the _____ Depression. These techniques are known as ______ _______. A time _____ can be carefully chosen to exclude the impact of a major event right outside it. And picking specific data ______ can hide important changes in between. Even when there's nothing wrong with the _____ itself, _______ out ________ data can give a __________ impression. This chart of how many people watch the Super Bowl each year makes it look like the event's __________ is exploding. But it's not accounting for __________ ______. The _______ have actually held steady because while the ______ of football fans has increased, their share of overall __________ has not. Finally, a graph can't tell you much if you don't know the full significance of what's being presented. Both of the following graphs use the same ocean temperature data from the National Centers for Environmental Information. So why do they seem to give opposite impressions? The first graph plots the _______ annual ocean temperature from 1880 to 2016, making the change look insignificant. But in fact, a rise of even half a degree Celsius can cause massive ecological disruption. This is why the second graph, which show the average temperature variation each year, is far more significant. When they're used well, graphs can help us intuitively _____ complex data. But as visual software has enabled more usage of graphs throughout all media, it's also made them easier to use in a careless or _________ way. So the next time you see a graph, don't be swayed by the lines and curves. Look at the labels, the _______, the scale, and the context, and ask what story the picture is trying to tell.

Solution

claimed
average
compressing
numbers
number
graph
toyota
afterall
ratings
dishonest
destroy
plenty
compared
range
grasp
graphs
viewership
leaving
points
relevant
cherry
population
accompanied
showing
march
great
popularity
picking
misleading
growth
trucks
american

Original Text

A toothpaste brand claims their product will destroy more plaque than any product ever made. A politician tells you their plan will create the most jobs. We're so used to hearing these kinds of exaggerations in advertising and politics that we might not even bat an eye. But what about when the claim is accompanied by a graph? Afterall, a graph isn't an opinion. It represents cold, hard numbers, and who can argue with those? Yet, as it turns out, there are plenty of ways graphs can mislead and outright manipulate. Here are some things to look out for. In this 1992 ad, Chevy claimed to make the most reliable trucks in America using this graph. Not only does it show that 98% of all Chevy trucks sold in the last ten years are still on the road, but it looks like they're twice as dependable as Toyota trucks. That is, until you take a closer look at the numbers on the left and see that the figure for Toyota is about 96.5%. The scale only goes between 95 and 100%. If it went from 0 to 100, it would look like this. This is one of the most common ways graphs misrepresent data, by distorting the scale. Zooming in on a small portion of the y-axis exaggerates a barely detectable difference between the things being compared. And it's especially misleading with bar graphs since we assume the difference in the size of the bars is proportional to the values. But the scale can also be distorted along the x-axis, usually in line graphs showing something changing over time. This chart showing the rise in American unemployment from 2008 to 2010 manipulates the x-axis in two ways. First of all, the scale is inconsistent, compressing the 15-month span after March 2009 to look shorter than the preceding six months. Using more consistent data points gives a different picture with job losses tapering off by the end of 2009. And if you wonder why they were increasing in the first place, the timeline starts immediately after the U.S.'s biggest financial collapse since the Great Depression. These techniques are known as cherry picking. A time range can be carefully chosen to exclude the impact of a major event right outside it. And picking specific data points can hide important changes in between. Even when there's nothing wrong with the graph itself, leaving out relevant data can give a misleading impression. This chart of how many people watch the Super Bowl each year makes it look like the event's popularity is exploding. But it's not accounting for population growth. The ratings have actually held steady because while the number of football fans has increased, their share of overall viewership has not. Finally, a graph can't tell you much if you don't know the full significance of what's being presented. Both of the following graphs use the same ocean temperature data from the National Centers for Environmental Information. So why do they seem to give opposite impressions? The first graph plots the average annual ocean temperature from 1880 to 2016, making the change look insignificant. But in fact, a rise of even half a degree Celsius can cause massive ecological disruption. This is why the second graph, which show the average temperature variation each year, is far more significant. When they're used well, graphs can help us intuitively grasp complex data. But as visual software has enabled more usage of graphs throughout all media, it's also made them easier to use in a careless or dishonest way. So the next time you see a graph, don't be swayed by the lines and curves. Look at the labels, the numbers, the scale, and the context, and ask what story the picture is trying to tell.

Frequently Occurring Word Combinations

ngrams of length 2

collocation	frequency
ways graphs	2
data points	2
ocean temperature	2

Important Words

accompanied
accounting
ad
advertising
afterall
america
american
annual
argue
assume
average
bar
barely
bars
bat
biggest
bowl
brand
carefully
careless
celsius
centers
change
changing
chart
cherry
chevy
chosen
claim
claimed
claims
closer
cold
collapse
common
compared
complex
compressing
consistent
context
create
curves
data
degree
dependable
depression
destroy
detectable
difference
dishonest
disruption
distorted
distorting
easier
ecological
enabled
environmental
event
exaggerates
exaggerations
exclude
exploding
eye
fact
fans
figure
finally
financial
football
full
give
graph
graphs
grasp
great
growth
hard
hearing
held
hide
immediately
impact
important
impression
impressions
inconsistent
increased
increasing
information
insignificant
intuitively
job
jobs
kinds
labels
leaving
left
line
lines
losses
major
making
manipulate
manipulates
march
massive
media
mislead
misleading
misrepresent
months
national
number
numbers
ocean
opinion
outright
people
picking
picture
place
plan
plaque
plenty
plots
points
politician
politics
popularity
population
portion
preceding
presented
product
proportional
range
ratings
relevant
reliable
represents
rise
road
scale
share
shorter
show
showing
significance
significant
size
small
software
sold
span
specific
starts
steady
story
super
swayed
tapering
techniques
tells
temperature
ten
time
timeline
toothpaste
toyota
trucks
turns
unemployment
usage
values
variation
viewership
visual
watch
ways
wrong
year
years
zooming