How to lose your job in 1 dataviz mistake

Imagine this: you're at work, and your boss drops a new dataset on your desk.

It's straightforward—just a list of how long 400,000 people took to run a marathon.

The ask? Plot the distribution of those finishing times.

Too easy

In French, we'd say "finger in the nose" (though that doesn't quite translate well into English).

Anyway, I'm sure you know how to make a histogram, and if not, I've written plenty of tutorials using R, Python, or JavaScript to guide you.

A few minutes later, you end up with a neat histogram that looks like an almost normal distribution:

Job done!

Yes but!

You might think the job is done already.

But there's a big problem here.

Can you spot it?

Take a moment to think before scrolling! Here is an image to make sure you do not see the answer right away.

Did you miss the key story?

By changing the bin size, a whole new story emerges.

Suddenly, distinct spikes appear around 3:00, 3:30, and 4:00 hours.

Why?

Because these are popular time goals for marathon runners, and they push hard to finish at these times. So, it's much more common for someone to finish around 4:00 than at, say, 4:05.

If you want to explore this yourself, I've created an interactive version of the chart where you can adjust the bin size with a slider.

Interactive version with slider

This discovery is so fascinating that even The New York Times wrote an article about it.

Avoid common pitfalls

A great way to improve your data visualization skills is by avoiding common pitfalls like this one.

I've put together a collection of these pitfalls and plan to grow it even further. Each one will have its own flashcard, summarizing the context.

Before I move forward, I'd love your feedback on the design and concept of the first card:

Can cards like this be useful? Like the design of this one? I would be so grateful if you drop me an email to tell me what you think!

See you next Saturday!
Yan