How to spot a misleading graph Lea Gaslowitz
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.