How to spot a misleading graph - Lea Gaslowitz
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0:08 - 0:11A toothpaste brand claims
their product will destroy more plaque -
0:11 - 0:13than any product ever made.
-
0:13 - 0:16A politician tells you their plan
will create the most jobs. -
0:16 - 0:19We're so used to hearing these
kinds of exaggerations -
0:19 - 0:21in advertising and politics
-
0:21 - 0:23that we might not even bat an eye.
-
0:23 - 0:26But what about when the claim
is accompanied by a graph? -
0:26 - 0:28Afterall, a graph isn't an opinion.
-
0:28 - 0:33It represents cold, hard numbers,
and who can argue with those? -
0:33 - 0:36Yet, as it turns out, there are plenty
of ways graphs can mislead -
0:36 - 0:38and outright manipulate.
-
0:38 - 0:41Here are some things to look out for.
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0:41 - 0:46In this 1992 ad, Chevy claimed to make
the most reliable trucks in America -
0:46 - 0:48using this graph.
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0:48 - 0:52Not only does it show that 98% of all
Chevy trucks sold in the last ten years -
0:52 - 0:54are still on the road,
-
0:54 - 0:57but it looks like they're twice
as dependable as Toyota trucks. -
0:57 - 1:01That is, until you take a closer look
at the numbers on the left -
1:01 - 1:05and see that the figure for Toyota
is about 96.5%. -
1:05 - 1:09The scale only goes between 95 and 100%.
-
1:09 - 1:13If it went from 0 to 100,
it would look like this. -
1:13 - 1:16This is one of the most common
ways graphs misrepresent data, -
1:16 - 1:18by distorting the scale.
-
1:18 - 1:21Zooming in on a small portion
of the y-axis -
1:21 - 1:26exaggerates a barely detectable difference
between the things being compared. -
1:26 - 1:28And it's especially misleading
with bar graphs -
1:28 - 1:31since we assume the difference
in the size of the bars -
1:31 - 1:33is proportional to the values.
-
1:33 - 1:36But the scale can also be distorted
along the x-axis, -
1:36 - 1:40usually in line graphs
showing something changing over time. -
1:40 - 1:45This chart showing the rise
in American unemployment from 2008 to 2010 -
1:45 - 1:48manipulates the x-axis in two ways.
-
1:48 - 1:50First of all, the scale is inconsistent,
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1:50 - 1:53compressing the 15-month span
after March 2009 -
1:53 - 1:57to look shorter than
the preceding six months. -
1:57 - 2:00Using more consistent data points
gives a different picture -
2:00 - 2:04with job losses tapering off
by the end of 2009. -
2:04 - 2:07And if you wonder why
they were increasing in the first place, -
2:07 - 2:11the timeline starts immediately after
the U.S.'s biggest financial collapse -
2:11 - 2:13since the Great Depression.
-
2:13 - 2:15These techniques are known as
cherry picking. -
2:15 - 2:19A time range can be carefully chosen
to exclude the impact of a major event -
2:19 - 2:21right outside it.
-
2:21 - 2:25And picking specific data points
can hide important changes in between. -
2:25 - 2:27Even when there's nothing wrong
with the graph itself, -
2:27 - 2:31leaving out relevant data can give
a misleading impression. -
2:31 - 2:34This chart of how many people watch
the Super Bowl each year -
2:34 - 2:38makes it look like the event's
popularity is exploding. -
2:38 - 2:40But it's not accounting
for population growth. -
2:40 - 2:42The ratings have actually held steady
-
2:42 - 2:45because while the number
of football fans has increased, -
2:45 - 2:48their share of overall viewership has not.
-
2:48 - 2:50Finally, a graph can't tell you much
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2:50 - 2:53if you don't know the full significance
of what's being presented. -
2:53 - 2:56Both of the following graphs
use the same ocean temperature data -
2:56 - 3:00from the National Centers
for Environmental Information. -
3:00 - 3:02So why do they seem to give
opposite impressions? -
3:02 - 3:05The first graph plots the average
annual ocean temperature -
3:05 - 3:08from 1880 to 2016,
-
3:08 - 3:10making the change look insignificant.
-
3:10 - 3:13But in fact, a rise of even
half a degree Celsius -
3:13 - 3:16can cause massive ecological disruption.
-
3:16 - 3:17This is why the second graph,
-
3:17 - 3:20which show the average temperature
variation each year, -
3:20 - 3:22is far more significant.
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3:22 - 3:27When they're used well, graphs can
help us intuitively grasp complex data. -
3:27 - 3:31But as visual software has enabled
more usage of graphs throughout all media, -
3:31 - 3:36it's also made them easier to use
in a careless or dishonest way. -
3:36 - 3:40So the next time you see a graph,
don't be swayed by the lines and curves. -
3:40 - 3:41Look at the labels,
-
3:41 - 3:42the numbers,
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3:42 - 3:43the scale,
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3:43 - 3:44and the context,
-
3:44 - 3:47and ask what story the picture
is trying to tell.
- Title:
- How to spot a misleading graph - Lea Gaslowitz
- Speaker:
- Lea Gaslowitz
- Description:
-
View full lesson: http://ed.ted.com/lessons/how-to-spot-a-misleading-graph-lea-gaslowitz
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 has also made them easier to use in a careless or dishonest way — and as it turns out, there are plenty of ways graphs can mislead and outright manipulate. Lea Gaslowitz shares some things to look out for.
Lesson by Lea Gaslowit, animation by Mark Phillips.
- Video Language:
- English
- Team:
- closed TED
- Project:
- TED-Ed
- Duration:
- 04:10
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