Our favourite communicator of risk talks about the statistics of COVID-19, the quality of government briefings, and how to counter misinformation.

What do statisticians do in the pharmaceutical industry?

Why a positive test result doesn't necessarily mean you have the disease.

It would be foolish to ignore evidence. Luckily Bayes' theorem shows us how to take it in into account.

The company 23andMe made headlines by launching its DNA testing service in the UK. But how are the risks of developing a disease calculated?

Florence Nightingale died a hundred years ago, in August 1910. She survives in our imaginations as an inspired nurse, who cared passionately for injured and dying soldiers during the Crimean war, and then radically reformed professional nursing as a result of the horrors she witnessed. But the "lady with the lamp" was also a pioneering and passionate statistician. She understood the influential role of statistics and used them to support her convictions. So to commemorate her on the centenary of her death, we'll have a look at her life and work as a statistician.

How do you judge the risks and benefits of new medical treatments, or of lifestyle choices? With a finite health care budget, how do you decide which treatments should be made freely available on the NHS? Historically, decisions like these have been made on the basis of doctors' individual experiences with how these treatments perform, but over recent decades the approach to answering these questions has become increasingly rational.

Infectious diseases hardly ever disappear from the headlines — swine flu is only the last in a long list containing SARS, bird flu, HIV, and childhood diseases like mumps, measles and rubella. If it's not the disease itself that hits the news, then it's the vaccines with their potential side effects. It can be hard to tell the difference between scare mongering and responsible reporting,
because media coverage rarely provides a look behind the scenes. So how do scientists reach the conclusions they do?

Lack of statistical detail leads to wrong conclusions

Which drug should you choose if your budget is finite?

When is a medical treatment worth its cost?

How to evaluate medical treatments