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What to teach in a statistics course for journalists?

Pascal Biber writes:

I am a science journalist for Swiss public television and have previously regularly covered the “crisis in science” on Swiss public radio, including things like p-hacking, relative risks, confidence intervals, reproducibility etc.

I have been giving courses in basic statistics and how to read scientific studies for Swiss journalists without science backgrounds. As such, I am increasingly wondering what to teach (knowing they will not dive into Bayesian statistics). How should a non-science journalist handle p values? Should he at all? What about 95 percent confidence intervals? Should relative risks be reported when absolute numbers aren’t available? What about small studies? Should they even report about single studies? And how about meta-analysis?

I wondered if you have some basic advise for journalists that you could share with me? Or are you aware of good existing checklists?

To start with, here’s my article from 2013, “Science Journalism and the Art of Expressing Uncertainty.”

Also relevant is this post on Taking Data Journalism Seriously. And this on what to avoid.

There are some teaching materials out there, which you can find by googling *statistics for journalists* or similar terms, but there is a problem that once you try to get in deeper, there’s little agreement in how to proceed.

I suppose that the starting point is understanding the statistical terms that often arise in science: sample and population, treatment and control, randomized experiment, observational study, regression, probability.

Should you teach p-values and 95% intervals? I’m not sure. OK, yeah, you have to say something about these, as they appear in so many published papers. And then you have to explain how these terms are defined. And then you have to explain the problems with these ideas. I think there’s no way around it.

Bayesian methods? Sure, you have to say something about these, because, again, they’re used in published papers. You don’t have to “dive” into Bayesian methods but you can and should explain the idea of predictive probabilities such as arise in election forecasts.

For the big picture, I recommend this bit from the first page of Regression and Other Stories:


  1. I appreciate that 3rd challenge especially.

  2. Exactly once we get deeper the slog gets deeper.

  3. David Pittelli says:

    First, you need to understand two XKCD comics. Once you’ve done that you’re ahead of the curve.


    Frequentists vs. Bayesians

  4. dearieme says:

    I’d start with Gerd Gigerenzer’s book on Risk. Any journalist who absorbs its teachings can at least be confident of being more expert in stats than the average doctor. Or the very-much-above average doctor, probably.

    I’d also consider using some anecdotes from Malcolm Kendrick’s Doctoring Data.

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