Numbers, like those in polling and survey audiences, population samples, and research cohorts, are not inherently objective or neutral. They are a sample of real humans and the product of myriad factors. And just because big data is big does not mean that it, or algorithms that stem from large data sets, are representative or unbiased.
How do journalists fairly use numbers in reporting? What does it really mean for a sample to be representative? In what ways can reporters vet numbers quickly and reliably for potential bias?
Join the National Press Club Journalism Institute and the National Association of Science Writers for a program that will answer these questions and leave participants with new tactics to:
- Detect deep bias in numbers before they use them
- Surface hidden traps and avoid them
- Accurately represent the people and lives reflected in the data
Registration is open for this program, which will take place on Wednesday, August 11 at 11:30 a.m. ET.
- Fernand Amandi, managing partner of Bendixen & Amandi, the nation’s leading multilingual and multiethnic public opinion research and strategic communications consulting firm
- Caroline Chen, health care reporter at ProPublica, and 2019 winner of the June L. Biedler Cancer Prize for Cancer Journalism for her series with Riley Wong on racial disparities in clinical trials
- Dr. Kyler J. Sherman-Wilkins, assistant professor in the Sociology and Anthropology department at Missouri State University and a Mellon Emerging Faculty Leader for 2021
If you have questions about this program, please email Julie Moos, Institute executive director, at jmoos@press.org.