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