# Lying with Statistics

Lying with statistics is something that has been around for a long time now, charts tend to spread fast and widely. We see them everywhere, however, some of them don’t tell the truth. Thus, it’s important today to quickly determine if a graph is telling the truth. Let’s have a look at some examples of lies of visualization. By Nathan Yau

Bar charts use length as their visual cue, so when someone makes the length shorter using the same data by truncating the value axis, the chart dramatizes differences. Someone wants to show a bigger change than is actually there.

By using dual axes, the magnitude can shrink or expand for each metric. This is typically done to imply correlation and causation. “Because of this, this other thing happened. See, it’s clear.”

The spurious correlations project by Tyler Vigen is a great example.

Some charts specifically show parts of a whole. When the parts add up to more than the whole, this is a problem. For example, pie charts represent 100 percent of something. Wedges that add up to more than that? Peculiar.

Everything is relative. You can’t say a town is more dangerous than another because the first one had two robberies and the other only had one. What if the first town has 1,000 times the population that of the first? It is often more useful to think in terms of percentages and rates rather than absolutes and totals.

xkcd said it best.

It’s easy to cherrypick dates and timeframes to fit a specific narrative. So consider history, what usually happens, and proper baselines to compare against.

Interesting things can show up when you look at the big picture.

When you see a three-dimensional chart that is three dimensions for no good reason, question the data, the chart, the maker, and everything based on the chart.

As a rule of thumb, scrutinize charts that shock or seem more dramatic than you thought.