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At The Economist, we take data visualisation seriously. Every week we publish around 40 charts across print, the website and our apps. With every single one, we try our best to visualise the numbers accurately and in a way that best supports the story. But sometimes we get it wrong. We can do better in future if we learn from our mistakes - and other people may be able to learn from them, too. After a deep dive into our archive, I found several instructive examples. I grouped our crimes against data visualisation into three categories: charts that are (1) misleading, (2) confusing and (3) failing to make a point. For each, I suggest an improved version that requires a similar amount of space - an important consideration when drawing charts to be published in print.

Wrapping your head around the scale of a global pandemic is not easy, and the volume of stats and data can be bewildering. What, for instance, are we to make of the fact Australia recorded just 109 new cases in its daily count for April 6? Given this figure peaked at around 400 new cases per day, does this mean the rate of infection is now tapering off? And what, apart from sadness, are we to make of more gruesome statistics, such as the 969 COVID-19 deaths reported in a single day in Italy on March 27? To help interpret and understand the mountains of COVID-19 data, we'll look at five commonly used methods, and explain the pros and cons of each.