Behind the numbers

Country/area: United Kingdom

Organisation: The Times

Organisation size: Big

Publication date: 7 Apr 2020

Credit: Rosa Ellis, Tom Calver, Sam Joiner, Ryan Watts

Project description:

Last year data was thrust into the spotlight like never before and we felt our readers would benefit from context and explanations of what the global pandemic meant for them. 

Behind the Numbers was a new series which aimed to explain what the figures really meant – from why counting deaths is not straightforward to how many people were being tested. 

Impact reached:

Each story in the series was published to coincide with the broader news agenda. In April we explained why calculating the death toll is so difficult and we looked at why men were dying at higher rates than women. In May we wrote about why the death count was likely to be higher than the official figures and looked at how the data on the number of people being tested was flawed. 

Each story performed extremely well with readers so we expanded the series to look at other topics, starting with what the data told us about racism in the criminal justice system. 

Techniques/technologies used:

These stories involved thoroughly researching all the datasets available and putting them in context for readers. We spoke to eminent statisticians in the UK to build up a picture of what the numbers meant for readers’ lives. 

For our story on the true UK coronavirus death toll we used modelling to project how many people were likely to have died by that date based on data that had a two-week lag. 

What was the hardest part of this project?

The series was based on government data, but the UK government did not always publish it in a way that was easy to comprehend (intentionally it seemed at times). We had to speak to top statisticians to gain context on what the government was not publishing and explain to readers what this meant. 

What can others learn from this project?

Good data journalism is not always about sophisticated methods of obtaining or analysing data. Sometimes it is about explaining clearly and concisely the numbers that everyone is looking at in order to put them in context for readers. 

Project links: