This post by Carla Pedrete is titled Common mistakes journalists make when using statistics, but I’d wager most statements wouldn’t be less true if extended to the general population (emphases mine):
Political parties and corporations use statistics to defend or justify particular interests. Hence, it is indispensable that journalists analyse numbers as deeply as they analyse words. David Sibert adds that it is socially unacceptable to admit that one is poor at reading or spelling, but people will proudly boast: “I’m no good at maths”. Journalists could actually use their position to help stop numerical illiteracy. (…) For Maria Ottati, the main problem is media talk about risks without taking into account the amount of people affected. For instance, the headline “Eating food X doubles the risk of developing cancer type Y” could be correct, but the article must explain that “cancer type Y is extremely rare and the risk of developing it is 1 in 100 million, so that a 50% increase is pretty meaningless”. (…) Paul Kingswell notes how journalists analyse a trend through the highest/lowest rates nationally, but without contextualising the data.
Other topics: percents versus percentage points, the use of absurd formula to impress, not rounding numbers. Indeed, as a supposedly empowered and informed society we have a long way to go to spread and deepen data literacy, a key skill for this century.
You know, reproducible data-driven journalism workflows, newspapers with GitHub accounts, R and Python repos, Jupyter notebooks. They’re all good. But I have the gnawing feeling we miss the mark of what is needed by the majority of people so tremendously.