Awful Data Visualizations

Not a bad visualization so much as a bad model:

(from 2017):

from here:

The ranking is pointless. You have no idea of where the life expectancy is.

The execution is screwed up - check out what’s going on in 1950.


AND they should move the labels to the right hand side and put the labels next to their respective lines so I don’t have to keep looking up and down. And get rid of those horizontal dashed lines, which are useless and just distract.

It looks like the life expectancy of US fell to near zero. That’s probably right around the time when the fungus from The Last Of Us first appeared.


I will give the article where it’s from, and it doesn’t really matter what is being compared, but this is frickin deceptive (yes, I will be writing a blog post about this)

look at the horizontal axis



graph with fixed axis

The old “change the scale halfway through the graph” trick.

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I really wish I had time to become expert at data visualization. Too many books to read and stuff to learn unfortunately. It’s cool watching it on the sidelines though.

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Second time I’ve fallen for that this month!

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Now, do the same awful-and-corrected versions of “Deaths by shark attack vs Death by drowning.”

Same. One of my team members is really good at it and gave a one-hour presentation and I’m trying to use that and at least get better. Not great, but better than I was yesterday, and that’s something.

You have a link/graph I can do this with?

I have plenty of drowning graphs.

I do not have any shark attack graphs because:

32 deaths (and that could be from whales, or seals or walruses… not just sharks) for a period of 22 years.

Here’s death by dog for the same period:

Okay, I’m doing a 1-hour presentation at the SOA Virtual Annual Meeting this year:

VIRTUAL - Session 3B: Communicate Results with Effective Data Visualization


Credits: 1.20 SOA CPD; 1.00 CIA

Competency/Skills Based Learning: Communication and Influencing Informed Decision Ma

Session Description: One of the most effective ways to convey quantitative results is via data visualization. However, many times actuaries have been ineffective in doing this. In this session, you will see a demonstration of certain dataviz choices that make communicating results more effective in terms of graph type choice, graph element styles, colors and more. All examples will be in Excel-and doesn’t require Visual Basic for Applications (VBA)! Nothing too fancy! I want to distribute working files to attendees before the session so they can follow along with how to build the graphs, make changes, etc. Graph choices will include line graph, tile grid map and jitter graph.

Country Relevance: Non-Nation Specific

Experience Level: Beginner-intermediate


There are legitimate uses of different vertical scales for two series on the same graph

this ain’t it

an honest (and ugly) version of the real data:

Could have at least used the Isfahan theme, imo.

A current single-payer-fan rant is based on a graph that shows the lower USA life expectancy compared to life expectancy in other countries. They claim that this is the fault of the USA health insurance industry, which is failing to improve life expectancy despite USA healthcare costing more than in other countries with higher life expectancies.

It’s not entirely false. I know a couple of people who died younger than they should have because they had inadequate health insurance.

…just a passing thought…


If you care to share, what condition was it and what treatment did they not receive?

One had breast cancer which might have been treatable, but the odds were only so-ao, and she decided not to impoverish her family. Her insurance would only have paid half the cost. That was before Obamacare. The other guy was self-employed, and didn’t buy health insurance. He dropped dead in his 50s from a heart attack, because he didn’t want to go to a doctor when he felt terrible as it started. I know a guy who was working with him the day before he died, and tried to talk him into seeing a doctor. But he didn’t want to pay for it, and decided to tough it out.