The Data Detective

A surprising statistical claim is a challenge to our existing worldview. It may provoke an emotional response—even a fearful one.

Sampling error is when a randomly chosen sample doesn’t reflect the underlying population purely by chance; sampling bias is when the sample isn’t randomly chosen at all.

Second, keeping score was important.

There are some overtly racist and sexist people out there—look around—but in general what we count and what we fail to count is often the result of an unexamined choice, of subtle biases and hidden assumptions that we haven’t realized are leading us astray.

Data visualization ducks can be more than tasteless: the duckness of the graph can actually obscure—or worse, it can misrepresent—the underlying information.

There is nothing wrong with the idea that government should collect statistics to inform itself. But there is a risk that this view slips into a proprietorial sense of ownership, when politicians believe not only that they should be using statistics to run the country, but that those statistics are none of anyone else’s business, and that external

Much of the data visualization that bombards us today is decoration at best, and distraction or even disinformation at worst. The decorative function is surprisingly common, perhaps because the data visualization teams of many media organizations are part of the art departments. They are led by people whose skills and experience are not in statisti

superforecasting means being willing to change your mind.

Be curious. Look deeper and ask questions.