See an Exciting Trend in That Chart? Proceed with Caution. - Deepstash
Ultimate Guide to Reducing Churn

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Ultimate Guide to Reducing Churn

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Correlation isn't causation

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.”


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Line Charts, Circles and Bars 📈📊

Line Charts, Circles and Bars 📈📊

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


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More Aggregation, More Assumptions of Causality

More Aggregation, More Assumptions of Causality

The same experiment was done, this time they developed bar charts, line charts, and scatterplots with three different levels of aggregation.

This time the type of chart had only a small effect on participants’ ratings of causality. Much more significant, however, was the effect of aggregation level. Across all different visualization styles, people saw aggregated data as more causal than less aggregated data.


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How to Visualize Responsibly

Be mindful when you aggregate, because the assumption people have may become a causal one. So think carefully about how you present your data and maybe iterate with your own design team or with your data analysts so people don’t misinterpret what you’re trying to say.

Even the most sophisticated tool rarely, on its own, identifies causation. “It’s just really, really good at finding trends.

So if you see an exciting trend, don’t automatically assume causality, Shapiro cautions instead, look at it skeptically. 


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