Statistics

When the underlying data they rely on is incomplete—and it often is—the growing use of machine learning tools in America's criminal justice system can have devastating effects.

Overdoses on opioid drugs—including heroin, prescription painkillers, and certain street drugs—were the top cause of unintentional, preventable deaths in the United States in 2017.

But that's not necessarily a bad thing.

The reproducibility crisis is driven, in part, by invalid statistical analyses that are from data-driven hypotheses.

Environmental Protection Agency enforcement fell to 10-year lows in 2017, but 2018's numbers may be even lower.

City, state, tribal, and other law enforcement agencies reported more than 7,000 hate crimes to the FBI in 2017, according to numbers the FBI released on Tuesday.

The city has reputation for violence, but without some of the best trauma centers the situation could be much worse.

Statisticians have shown that many scientific findings are wrong, and without an increase in statistical know-how for scientists it'll continue happening.

The polls weren’t (completely) wrong.

An applied mathematician wonders whether his environmental management models are doing anyone any good. Turns out, they probably are.

Statisticians say it's time to end the cult of "statistical significance."

When we treat people as collections of statistics, we ignore important aspects of their humanity.

Remembering the difference between mean and median.

Modern statistics have made it easier than ever for us to fool ourselves.

What does it take to break into the growing field of data analysis? To start, you'll need the Internet, a computer, and some basic math skills.

Athletes and fans look at sports differently. Ryan O'Hanlon, who is not a professional athlete, and Clint Irwin, who is a professional athlete, wonder if the two sides can ever be separated.

The count of the dead in Syria and other global trouble spots matters a great deal, and so does the provenance of the numbers presented. Here's one exhaustive effort to get them right.

No bowling alleys, no summoning demons, and no troubled superheroes.

How an equation cooked up by Mussolini’s numbers guy came to define how we think about inequality—from Occupy Wall Street to the World Bank to the billionaires at Davos—and why it’s time to find a new way of looking at the numbers.