
Ways to Lie with Charts (2014) - raldu
http://nautil.us/issue/19/illusions/five-ways-to-lie-with-charts
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lkozma
A long time ago I had taught a class on data visualization, and used this
photo as example of creative use of pie charts:

[https://www.answerminer.com/static/489716080133492e99fdcb9c8...](https://www.answerminer.com/static/489716080133492e99fdcb9c849e0110/f11ad/chart2.png)

Here was another list:
[http://avoinelama.fi/hingo/kirjoituksia/misleadingvisualizat...](http://avoinelama.fi/hingo/kirjoituksia/misleadingvisualizations.html)

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umvi
What's wrong with the pie chart?

Edit: I should have read the article before the comments... Apple is using the
thick wedge of foreground slice to make their share seem bigger

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QuinnWilton
I think they're also lumping Android into the other category.

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evan_
Not sure when that was taken but it’s totally possible that Android was
legitimately a tiny portion of the market or even not yet released

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_vk_
Judging from the fact that _RIM_ (the makers of the Blackberry line of
devices) is shown to have 39% of the market in that chart, this must've been
quite a while ago indeed.

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EricE
For anyone interested in not lying with charts:
[https://www.edwardtufte.com/tufte/](https://www.edwardtufte.com/tufte/)

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ragona
This reminds me of one of my favorite internal wiki posts at the megacorp I
work at. A particularly senior engineer (who was known for being witty and
quick with puns) had written an article titled something along the lines of,
“How to lie with iGraph” or maybe “lies, damn lies, and operational metrics.”
I don’t quite remember the title, but it was fully of hilarious and quite
specific tricks relating to our internal graphing software, and it came with a
ridiculous narrative written in his characteristic style. I should find that
and send it to my team tomorrow. It contains great information on how to avoid
making hard to read graphs, and how to spot bad ones. It also happens to be an
amazing primer on some of the more advanced features of the graphing
dashboard.

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S3raph
I can recommend a in my opinion very interesting book about "abusing stastics"
it's called "Standard Deviations: Flawed Assumptions, Tortured Data and Other
Ways to Lie with Statistics". (I don't know the author or have any affiliation
with it).

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greenyoda
See also the classic book _How to Lie with Statistics_ :
[https://en.wikipedia.org/wiki/How_to_Lie_with_Statistics](https://en.wikipedia.org/wiki/How_to_Lie_with_Statistics)

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yboris
I _highly_ recommend this short classic to everyone. A very fast read with
excellent, timeless examples. A must-read for every high schooler too!

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johnchristopher
The swindling shape example is needlessly pushing it by swapping colours.

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mirimir
Reporting on stock etc markets is so rife with "Honey, I Shrunk the Scale!"
that it's the norm.

And the suicide rate vs science and technology spending thing is a classic
example of correlation <> causation. In this case, I suspect that they're both
~population.[0]

0) [http://www.worldometers.info/world-population/us-
population/](http://www.worldometers.info/world-population/us-population/)

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benj111
I didn't find anything particularly wrong with that graph (suicide rate v
science)?

Sure if you use that data to say there is causation then that's wrong, but
there isn't anything wrong with the display of data.

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mirimir
TFA made the point that just showing the data that way leads people to think
there's causation.

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benj111
And in just the same way that you shouldn't believe a article saying there's
causation and just showing this graph as evidence. Then we shouldn't believe
this article when they make a statement like that.

Anyway it doesn't even make sense, causation means X _causes_ Y, the very next
question is which causes which? The graph has nothing to say on that. All the
graph implies is that there is a correlation, which isn't controversial
(scientifically, not socially speaking).

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mirimir
You and I, and most HN readers, are a lot better at making those sorts of
distinctions than the norm.

And correlation between science spending and suicide? I'd say that they both
correlate to population, so their relationship is just confounding. But then,
I'm no statistician.

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benj111
They didn't mention where you start the graph.

An origin of zero and and origin of 100 make something seem suitably high or
low.

And a global temperature graph starting from the late 90s will look different
to one starting at the beginning of the 20th century.

