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As an epidemiologist, I am interested in disease - and more specifically, who in a population currently has or might get that disease. What is their age, sex, or socioeconomic status? Where do they live? What can people do to limit their chances of getting sick? Questions exploring whether something is likely to happen or not can be answered with quantitative research. By counting and measuring, we quantify (measure) a phenomenon in our world, and present the results through percentages and averages. We use statistics to help interpret the significance of the results. While this approach is very important, it can't tell us everything about a disease and peoples' experiences of it. That's where qualitative data becomes important.

Lots of quantitative information in this video about the impact of sugar on health in the US. Using comedy/performance to make a point about statistics regarding a social/health issue.

This is a really great example of using a visualisation to communicate a quantitative fact check. This claim is a good case for doing a basic plausibility check, and thinking about what numeric information you'd need to know to understand the claim (e.g., how many people are deported now (what's the baseline), and what are the estimates for the maximum number of unauthorized immigrants in the country?).

"Significant Digits" is a daily digest of the numbers tucked inside the news by fivethirtyeight.com - e.g. in this issue 29 percent Percentage of Americans who regularly work weekends. Another 27 percent regularly work between the hours of 10 p.m. and 6 a.m. Maybe useful for understanding how important quantitative information is in the world around us.

In 1967, following riots in Detroit, Philip Meyer used survey research methods, powered by a computer, to show that college-educated people were just as likely to have rioted as high school drop outs. His story was one of the first examples of computer assisted reporting and precision journalism, in which journalists use social science methodologies to extract and tell stories. In recognition of his contribution to the area, each year's best computer-driven and precision stories are celebrated through the Philip Meyer Journalism Award. The Award's 2016 winners have just been announced, with the successful entries showcasing techniques derived from quantitative and qualitative methods, such as surveys using randomly-selected respondents, descriptive and inferential statistical analysis, social network analysis, content analysis, field experiments, and more.

The Supreme Court does not compute. Or at least some of its members would rather not. The justices, the most powerful jurists in the land, seem to have a reluctance - even an allergy - to taking math and statistics seriously. For decades, the court has struggled with quantitative evidence of all kinds in a wide variety of cases. Sometimes justices ignore this evidence. Sometimes they misinterpret it. And sometimes they cast it aside in order to hold on to more traditional legal arguments. (And, yes, sometimes they also listen to the numbers.) Yet the world itself is becoming more computationally driven, and some of those computations will need to be adjudicated before long. Some major artificial intelligence case will likely come across the court's desk in the next decade, for example. By voicing an unwillingness to engage with data-driven empiricism, justices - and thus the court - are at risk of making decisions without fully grappling with the evidence. This problem was on full display earlier this month, when the Supreme Court heard arguments in Gill v. Whitford, a case that will determine the future of partisan gerrymandering - and the contours of American democracy along with it. As my colleague Galen Druke has reported, the case hinges on math: Is there a way to measure a map's partisan bias and to create a standard for when a gerrymandered map infringes on voters' rights?