See an Exciting Trend in That Chart? Proceed with Caution.

According to new research from the Kellogg School, how data are visualized can significantly impact our interpretation of what we’re seeing sometimes for the worse. Our efforts to simplify charts by grouping data into smaller numbers of “buckets” (say, two bars on a chart as opposed to ten) seemed to lead people to mistake correlation for causation.

Graphs can be powerful, but it’s imperative that you know how to pick the right one for the right data. “You can lead your colleagues and customers to think more rationally about these kinds of problems.”

An experiment was done to find out whether these styles(bar charts, line charts, scatterplots, and plain text) differ in how "casual" they make the data appear to people.

4 correlations were chosen to display visually. 4 different visualizations for each scenario.

Each participant saw 4 styles one representation of each scenario.

After they controlled for the relative plausibility of each scenario, the type of style didn’t observably impact ratings

For the causal statements: Participants rated information they saw in bar charts as the most causal and scatter plots as the least causal