Journalists frequently use quantitative information in their stories. This includes statistics they find in reports produced by non-profit organizations, industry groups, and research centers, as well as data analyses that the journalist may have conducted themselves.
Such quantitative information can be highly useful to lend a news story greater authority, especially as numbers and statistics are often associated with being more neutral and objective than anecdotes and expertise. (This mythology is highly problematic, though. Quantitative information often have their own biases. For example, decisions about what to quantify about a phenomenon and how to measure it are still made by human beings. The measured information thus takes on some of its creator’s biases.)
In general, journalists are known to struggle with some core numeracy skills, such as calculating a percent change, differentiating a mean from a median, and determining a per capita rate. They are more likely yet to struggle with many applied statistical concepts, such as interpreting a margin of error or statistical significance (e.g., a p value). Journalists should not report quantitative information they do not understand, as that increases the likelihood they will misinform their audiences. Put another way, either seek out clarification in those cases, or omit your interpretation from the writing.
These struggles are compounded when it comes to the general public, which has, on average, even less training in numeracy. The average news consumer will not be familiar with statistical terminology and generally needs help to make sense of quantitative information (and, especially, to connect results from different statistical analyses).
Thus, knowing how to conduct (or even just interpret) quantitative analyses is only half the battle for a journalist. The second half is making such information comprehensible for a non-specialized audience.
Here are a few tips for effectively integrating quantitative information.
One key thing journalists bring to the table in the journalism-data relationship is the ability to identify what is most interesting about some dataset or analysis.
Datasets will often include many different variables about a large number of units of observation. Therefore, they can contain a large amount of interesting data points and trends. A scientific or industry report may similarly detail several findings of note. However, a journalist rarely has the space in a short story or broadcast segment to go into all of those potential findings.
Instead, your task as a journalist is to narrow things down to just a few results, relationships, or values that are especially interesting. Put another way, focus on just a few things and enrich them with detail, anecdotes, and context.
For example, the most interesting thing in a government report about crime in a city might be the increase in a few particular types of crimes. Rather than detailing the levels of all types of crime, focus just on the crimes that have increased at a disproportionate rate. (Conversely, maybe the story is that crime has generally remained flat over that period of time. If that’s the more important or representative story, focus on that.)
Alternatively, perhaps the most interesting thing in a dataset about crime is a single outlier. That is, maybe regional crime has stayed flat, with the exception of one particular city, which has seen a shocking increase in crime. How far is that outlier from the average? Why might that be the case? What is so unique about that outlier?
In deciding “what matters,” it is crucial that you keep your audience in mind. You may encounter a report or dataset that includes data for schools all around the country, but your audience will likely care most about how their local schools are faring. It might make sense to just focus on those few local schools, and perhaps even on just a few different measures that you consider to be most important in representing how those schools are faring.
When a journalist produces a regular news story for a typical outlet, they are usually producing it for someone with little more than a high-school education. The journalist will thus typically use short, declarative sentences and avoid jargon or esoteric language. Incorporating quantitative information into a story is no different. Chances are your audience will know little about regression analyses, or even fully understand what a statistical correlation entails.
Your job as a journalist is thus to simplify, simplify, and simplify in order to make sure your story can be understood by most adults. You can do this by including examples throughout the story to make the quantitative information easier to comprehend. For example, a massive devaluation of a foreign currency can sometimes be expressed in a more comprehensible way by describing how many hours a person would have to work at a minimum wage job in that foreign place in order to afford a cheese pizza or a cup of coffee.
Additionally, you should only include the methodological and statistical details that are essential to understanding a story, and express those details in an accessible way — even if it comes at some expense to precision. If you want to include the nitty-gritty details, it is often best to include it as a sidebar or as a companion (separate) methodological piece.
The only caveat here is if you are producing content for an outlet that has a numerically savvy audience that expects greater depth. This includes niche outlets that cater to experts (e.g., doctors) or particularly knowledgeable audiences (e.g., baseball junkies). Then, you can talk about the more complex details of particular analyses and methods, and use jargon those audiences are likely to understand.
Journalism is more than presenting facts — or, in this case, numbers. It is about helping individuals make sense of some phenomenon by showing them how the dots are connected.
When producing your story, ask yourself: How does this story help the audience better understand the issue? What does your reporting add to what’s already out there?
You can make your story insightful by focusing on the causes behind a trend identified in an industry report, or by focusing on the implications of your original data analysis. What might be driving the identified phenomenon? How might that phenomenon of interest affect the audience, or the people living in their communities?
For example, if you come across data showing that sexual assault cases are becoming more frequent in a particular county, you can produce an important story. However, that story can be made more useful if you’re able to identify what might be behind that increase in sexual assault cases (e.g., there were budget cuts at many police departments in that county that year). Similarly, you could perhaps use the report to point to existing resources and services for helping people deal with sexual assault, or connect it to a bill that might be under consideration that would change the funding for counseling or violence prevention programs.
Numbers can feel rather abstract and faceless, and staring at a bunch of them can make even the most ardent data lover’s eyes glaze after a while. Indeed, as a former ruler of the Soviet Union reportedly quipped once: “If only one man dies of hunger, that is a tragedy. If millions die, that’s only statistics.”
Great stories tend put a human face to the quantitative information in order to make it more relatable. This means using anecdotes and direct quotations from people who were affected by some issue or have intimate knowledge of that issue. Such anecdotes and quotes not only help break up the most informational parts of a story but they create opportunities to forge emotional connections with an audience.
It can often helpful to zoom in and out of stories by interchanging anecdotes and quantitative insights, such as by using an ‘accordion’ story structure. Quantitative information is often best used to illustrate the big picture and the trends; but it is often the human stories that help make the journalism compelling.
It is important to note that examples will sometimes come at the expense of analytic depth due to space constraints. For example, in order to make room for an anecdote, the journalist may need to cut some of the quantitative insights. This is a judgment call but it is important for journalists to remind themselves of the old adage: sometimes, less is more.
The use of quantitative information in a story often lends itself nicely to the inclusion of data visualizations and tables alongside the narrative. In fact, humans are much better at finding patterns, relationships, and making sense of a large number of data points when such information is presented visually. This is especially the case when there is a stark contrast between things, and the audience can be shocked by just taking a quick glance at two visuals that show a clear disparity.
Visuals should not simply duplicate prose, though. Instead, visuals should complement narratives. A common way to do this is to tell and show. Tell your audiences what you believe to be the most important take-away quantitative insights through your prose and show that point through a compelling anecdote in the narrative (and an accompanying data visualization that shows the relevant pattern).
If you are integrating quantitative information from an industry or research report, there’s a good chance a visualization already exists. However, that visualization may be too sophisticated for a general audience — it was likely designed with a specialized audience in mind — and you may thus need to recreate it in a more accessible (and, oftentimes, more aesthetically pleasing) way.
Visualizations can also be used as asides to a story, via the use of sidebars and the like. Such spaces are reserved for information that is important and relevant, but that might be too tangential (and thus disruptive) to include in the middle of a story. Additionally, visuals and tables can be useful tools for opening up a dataset to audiences and allowing them to draw their own inferences. This can be accomplished by either creating an online front-end for the dataset (e.g., a searchable database) or by creating an interactive visualization that allows someone to explore all of the data points. Such aides allow you to have a highly-focused story, but still permit the audience to identify new relationships or story angles on their own.
The most compelling and comprehensible stories tend to employ all of the above tactics. Put another way, these tactics shouldn’t be thought about in isolation but rather be employed in an integrated way. Try to focus on just a few things, simplify the information, make it insightful by connecting the dots, increase the relatability by including a human face, and take advantage of visual aides to express information.
Doing all of these things at once can prove to be a challenge to journalists, but it is a challenge that gets easier with time. However, even doing just a few of these things will go a long way to producing a story infused with quantitative information that general audiences can both learn from and enjoy.
Journalists and audiences alike struggle with numeracy skills. Journalists should not report quantitative information that they do not understand, as they risk misinforming their audiences.
While you may have access to a range of quantitative information in a report or dataset, your story will likely only end up focusing on a small subset of that information — namely, the most important or interesting findings. Oftentimes, less is more.
When integrating quantitative information, be sure to simplify it, connect the dots for the audience, seek human faces to exemplify it, and consider the use of visual aides.