Sometimes, the sheer size of a big number is hard to make sense of when we’re trading in millions of dollars or thousands of lives saved. One death is a tragedy; a million deaths is a statistic, or so the quote goes.

In our first Tiny Tip, we talked about being more critical readers of big numbers. What’s the source of the statistic? How does that number fit into bigger context? USAID’s $28.6 billion budget sounds like a lot of money, but is less than 1% of the total budget of the US government. Context matters.

Even with added context, $28.6 billion is still on a scale that might feel like Monopoly money compared to our household budgets. When we focus on summary stats for country or global programs, numbers often get so big they’re hard to wrap our heads around, which is why we often see big numbers paired with human interest stories, like in the PEPFAR Stories of Hope.

Let’s take a look at results from one big global program: the President’s Emergency Program for AIDS Relief (PEPFAR). In fiscal year 2024, most of the stats are in the millions: 20.6 million people on life-saving antiretroviral treatment, 83.8 million people provided with HIV-testing services, and the list goes on.

Global Results and Projections Fact Sheet (December 2024) (State Department)

As of September 30, 2024, PEPFAR has also enabled 7.8 million babies to be born HIV-free to mothers living with HIV, a tremendous achievement for curbing the spread of the virus and for the health of each of those infants (HIV.gov).

But as much as we love to celebrate achievements in global health, a major challenge we’ve faced the past two weeks is helping people make sense of just how disastrous the stop work order for USAID is for beneficiaries around the world. Anecdotal stories, like this one from Doreen about receiving prevention services during her pregnancy, take us to the individual level and can cultivate empathy, but sometimes we need tactics to make the big number more relatable.

Let’s unpack three common tactics data communicators can use to make big numbers relatable: frequency framing, reduce the scale, and comparing to a familiar unit.

Three Tactics to Make Data Relatable

1. Frequency framing

Reframing a percent into a number is data communication tactic called frequency framing. At it’s simplest, frequency framing is saying “1 in 4 people” instead of “25%".

Pause and consider this question: would you feel more concerned if you heard you had a 1 in 4 risk of developing colon cancer or a 25% lifetime risk?

Those are the actual stats for the lifetime risk of colon cancer for women in the United States.

Research has found we’re better able to make sense of stats and assess relative risk with data reported as frequencies. For me, when I hear 1 in 4, I can see myself sitting at my dining table with three friends. I can see the number represented in people: one in four of us in those chairs. It makes the odds that one of us would get sick feel a bit real compared to 25%, which somehow feels smaller.

We often see this frequency framing idea translated into graphics called isotype charts or icon matrixes. The icons can further humanize the numbers compared to a chart or percent; you’ll see data visualization designers using different approaches aesthetically, like the two examples below with a repeating, gender neutral icon and a mix of different representations of silhouettes.

In contrast to the icons, the donut chart may look more precise and scientific, but lacks that humanizing element.

2. Reduce the scale

Talking about big numbers can feel impossibly large: let’s go back to the stat that 7.8 million babies were born HIV-free thanks to PEPFAR.

Instead of focusing only on big, aggregate statistics, the New York Times took a different approach to making the case about the impact of USAID programs and the impact of those programs shutting down.

Using a set of text callouts pinned across a map, the designer Malika Khurana highlights a few illustrative stats about the impact of the funding pause. In each callout, the focus is on what’s happening in one country instead of the world. In the case of maternal to child transmission numbers, they focus on one day instead of the program’s history. “Aid workers in Uganda say about 40 newborns contracted HIV per day when the US stopped funding for antiretroviral drugs.”

Map from *How the World is Reeling from Trump’s Aid Freeze (New York Times*)

This more focused number is a scale that we could even visualize as a nursery full of infants, which helps us empathize with the people being impacted by funding decisions an ocean away.

3. Compare to a familiar unit

A third way we humanize big numbers is to break them down into familiar units often with some kind of visual metaphor.

You probably heard these when you were a kid before seeing them in the news or infographics. How long is a blue whale? Three school buses! What does a school bus have to do with a whale? Nothing, besides being a unit we are generally familiar with. Now, even if you’ve never seen a blue whale, now you have a better sense of just how big that animal is.

Talking about health care, we often see these comparisons when trying to make sense of the scale of lives lost from a disease.

How many plane crashes each day would equal the deaths due to COVID during the height of the pandemic? Or, how many times would a single event, like 9/11, need to repeat to hit the same number of lives lost?

Each of these comparisons focus on making an abstract number easier to understand. Often, that comes at the expense of some degree of precision though, which is a choice we should make purposefully. School buses can be different lengths and so can blue whales. The goal isn’t a precise measurement, but instead relative comparisons that make a data point easier to understand.

Why recognizing these data communication tactics matters

While data viz enthusiasts may love numbers, not everyone connects with ideas through an onslaught of stats and figures. We need to meet people where they are and present information in compelling, engaging ways. For data communicators, consider these three approaches next time you’re trying to communicate a big number in a more relatable way.

For readers, remember that the same approaches used to elicit understanding can also be used to minimize an issue or mislead. When you see these tactics in a news article, make the same checks you would of other data points: what’s the source, what is the message, and what is the creator’s intent? We’ll talk about how to dig into the source and intents later in this Tiny Tips series.