The Data Detective

As our communication, leisure, and commerce are moving to the internet, and the internet is moving into our phones, our cars, and even our spectacles, life can be recorded and quantified in a way that would have been hard to imagine just a decade ago.

Ideally, a decision maker or a forecaster will combine the outside view and the inside view—or, similarly, statistics plus personal experience. But it’s much better to start with the statistical view, the outside view, and then modify it in the light of personal experience than it is to go the other way around. If you start with the inside view you

Psychologists have a name for our tendency to confuse our own perspective with something more universal: it’s called “naive realism,” the sense that we are seeing reality as it truly is, without filters or errors.[9] Naive realism can lead us badly astray when we confuse our personal perspective on the world with some universal truth.

The most straightforward problem with a clever decorative idea is that the basic data may not be solid. The visualization then simply hides that fact—the shimmering icing over a moldering statistical cake.

Big found datasets can seem comprehensive, and may be enormously useful, but “N = All” is often a seductive illusion: it’s easy to make unwarranted assumptions that we have everything that matters.

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.

cheerleaders for big data have made three exciting claims, each one reflected in the success of Google Flu Trends. First, that data analysis produces uncannily accurate results. Second, that every single data point can be captured—the “N = All” claim we met in the previous chapter—making old statistical sampling techniques obsolete (what that means

A good chart isn’t an illustration but a visual argument,” declares Alberto Cairo near the beginning of his book How Charts Lie.

But bigger isn’t always better. It’s perfectly possible to reach vast numbers of people while still missing out on enough other people to get a disastrously skewed impression of what’s really going on.

anyone who is confident of the effectiveness of their algorithm should be happy to demonstrate that effectiveness in a fair and rigorous test.