Meaningful Math

Supporting adult statistical literacy through better journalism.

by Knology
Feb 8, 2023

To be a full participant in modern life, you have to be comfortable with quantitative data. Every time you enter a grocery store, see the doctor, or go to the bank, you're asked to make sense of numbers and statistics. But these things are frequently presented in ways that are difficult to understand.

This is something the news media can help with. The news is one of the most common places people encounter numerical evidence. Whether it be a story about financial markets, the weather, sports, voters’ opinions, or health, numbers are everywhere in the news. The abundance of quantitative data in the media gives journalists and news organizations an opportunity to support audiences’ quantitative reasoning skills. In support of these efforts, in 2019, we launched a project called “Meaningful Math.” With financial support from the National Science Foundation, and in collaboration with partners at PBS NewsHour, we’re working to:

  • Study the prevalence of statistics in the news
  • Study differences in adults’ confidence and competence with statistics
  • Support the development of news content that helps support adults’ numeracy skills

Meaningful Math asks questions such as:

Since the beginning of the Meaningful Math project in 2019, our work has been published and shared in a wide range of venues—including peer-reviewed academic journals, the Knology website, and a variety of media and media research websites. This landing page provides links to (and brief descriptions of) all of the major outputs associated with the Meaningful Math project. We are updating the following list as new research comes out, so be sure to check back here frequently!

The most important component of Meaningful Math is a guide for journalists. This living document breaks down a number of dos and don'ts, with examples from a broad range of news sources, and across a wide variety of topics. It includes separate sections on headlines, polling, health and medicine, climate, and economics. Designed to be practical, and to provide quick and effective solutions to common quantitative reporting problems, this guide's recommendations reflect the lived experiences of practicing journalists. Critically, it is centered in the professional constraints that journalists face in their day-to-day work.

A recording of the webinar introducing the guide and some of the overall tips is available online.

Along with peer-reviewed articles and pieces for our website, we also write essays and editorials that are featured in popular journals and media outlets. This section of our bibliography also includes links to coverage of our work by those outside the Meaningful Math team.

How People Interpret Charts (2023)

NewsHour's Laura Santhanam and Knology's Jena Barchas-Lichtenstein talked about what we've learned over these four years on the Stats + Stories podcast.

Does Green on COVID-19 Maps Mean What You Think? (2022)

This essay, published shortly after the CDC introduced its "COVID Community Levels" map, describes best practices around COVID data visualization that emerged over the course of the pandemic—and what happens when norms are broken.

On the Limits of Official Statistics (2021)

This personal essay uses COVID-19 statistics as an entry point to highlight what numbers cannot do, and to introduce the broad-ranging types of knowledge people use to make sense of them.

Omicron is Spreading Fast, But not as Fast as First Reported (2022)

In an editorial, two members of the team illustrate the need for better communication around uncertainty.

Can We Make it Easier for Readers to Digest all the Numbers Journalists Stuff into their Stories? (2022)

In this piece, the Nieman Lab discusses our peer-reviewed "Number Soup" paper, overviewing it in a more easily digestible format.

Our research has been published in numerous peer-reviewed scholarly journals, including Journalism Practice, Journalism Studies, and Numeracy. Particular studies of interest include:

Better News About Math: A Research Agenda (2021)

This "call to arms" paper highlights the need for more research on the relationship between news and quantitative literacy. While our own research addresses some of the questions posed here, there's still plenty of exploration to be done. Read a summary, or get the whole paper.

Surveying the Landscape of Numbers in U.S. News (2022)

We analyzed 230 news stories sampled over the course of a week in early 2020, as COVID started to spread across the world. We look at the quantitative content in each story at both an overall and phrase-by-phrase level, and the demands placed on readers across four news topics (Health, Science, Politics, and Business). Read a summary, or get the whole paper.

Number Soup: Case Studies of Quantitatively Dense News (2022)

This paper closely examines the most quantification-heavy excerpts from a sample of 230 news stories. We discuss patterns observed across these "dense" excerpts, and the demands they place on readers. Perhaps most importantly, we offer implications and recommendations for journalists and news outlets. Read a summary, or get the whole paper.

Examining the Relationship between Quantitative Reasoning Skills and News Habits (2023)

This paper explores the relationship between people’s news behaviors and their quantitative reasoning abilities. Looking at these behaviors across a variety of outlets, we identify six distinct clusters of news use patterns, and discuss how these patterns—along with demographic and affective factors—relate to quantitative reasoning performance.Read a summary, or get the whole paper.

A Three-dimensional Model of News Recirculation: Towards a Unified Understanding of News Sharing (2022)

This paper outlines a general model for news sharing behaviors, both digital and analog, based on data collected over the course of our partnership with NewsHour. Read a summary, or get the whole paper.

In addition to our peer-reviewed publications, we also regularly publish material for the Knology website. These articles often focus on the practical applications of our research findings, and are divided into three different series:

Numbers in the News

This series presents findings from studies testing small changes to real news stories, with the goal of helping readers draw more accurate inferences. Along the way, we identify some common sources of confusion. This series has resulted in the following reports:

  • Caveats and Credibility (May 2020) - In this report, we explore the effect of explicit caveats in news stories on audiences;
  • Understanding Estimates (July 2020) - In this report, we look at how audiences interpret the official statistics reported in news stories, and the effects of explaining how these estimates are calculated;
  • More about Official Statistics (October 2020) - In this report, we dig a little deeper into people’s understandings of official statistics, and share some findings based on a study of audience responses to a story about unemployment statistics;
  • Margin of Error (September 2020) - In this report, we look at how audiences interpret margin of error values reported in news stories, and what inferences they make as a result;
  • Social Desirability Bias (June 2022) - In this report, we discuss how audiences reason about trends in behavior based on national polls;
  • Interpreting Ambiguous Quantitative Statements (July 2022) - In this report, we highlight how people make sense of quantification without numbers;
  • Inflation Explanations (November 2022) - In this report, we share results from a study designed to determine if sidebar content can help news readers better understand quantitatively-dense stories on topics such as inflation.

Stats Sweep

This series highlights news about statistics that could have been more effective, and has resulted in the following articles:

  • Change over Time (July 2021) - This report analyzes CNN’s reporting on crime polls, and reveals things to do and not do when reporting on change over time in polls;
  • Numbers in Headlines (August 2021) - This report examines recent coverage of the Consumer Price Index, and reveals things to do and not do when reporting on indicator statistics provided by public agencies.

Behind the Research

In this series, different Knology researchers discuss their work processes, providing a “behind the scenes” look at how they gather, analyze, and interpret the data that goes into individual studies.

Attaway, B., Voiklis, J., Barchas-Lichtenstein, J., Hochberg, E., Hammerman, J., Thomas. U.G., LaMarca, N., Santhanam, L., & Parson, P. (2023). Examining the relationship between quantitative reasoning skills and news habits. Numeracy, 16(1). DOI: 10.5038/1936-4660.16.1.1430

Barchas-Lichtenstein, J. (2021). On the limits of official statistics. Public Anthropologies.

Barchas-Lichtenstein, J. (In press). A three-dimensional model of news recirculation: towards a unified understanding of news sharing. Journalism Studies. DOI: 10.1080/1461670X.2022.2150264

Barchas-Lichtenstein, J., & Ginsberg, D. (2022). Does green on COVID-19 maps mean what you think? Sapiens.

Barchas-Lichtenstein, J., & Voiklis, J. (2022). Omicron is spreading fast, but not as fast as first reported. Fast Company.

Barchas-Lichtenstein, J., Voiklis, J., Attaway, B., Santhanam, L., Thomas, U.G. Parson, P., Isaacs-Thomas, I., Ishwar, S., & Fraser, J. (2022). Number soup: Case studies of quantitatively dense news. Journalism Practice. DOI: 10.1080/17512786.2022.2099954

Barchas-Lichtenstein, J., Voiklis, J., Santhanam, L., Akpan, N., Ishwar, S., Attaway, B., Parson, P., & Fraser, J. (2021). Better news about math: A research agenda. Numeracy, 14(1), article 4. DOI: 10.5038/1936-4660.14.1.1377

Benton, J. (2022). “Number soup”: Can we make it easier for readers to digest all the numbers journalists stuff into their stories? Nieman Lab.

Voiklis, J., Barchas-Lichtenstein, J., Attaway, B., Thomas, U.G., Ishwar, S., Parson, P., Santhanam, L., & Isaacs-Thomas, I. (2022). Surveying the landscape of numbers in U.S. news. Numeracy, 15(1). DOI: 10.5038/1936-4660.15.1.1406

Funding Statement

These materials were produced for Meaningful Math, a research project funded through National Science Foundation Award #DRL-1906802. The authors are solely responsible for the content on this page.

Photo by Luis Cortés on Unsplash

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