As Joe Blitzstein likes to say, "Thinking conditionally is a condition for thinking." Humans are not naturally good at this skill. Consider the following example: Kelly is interested in books and keeping things organized. She loves telling stories and attending book clubs. Is it more likely that Kelly is a bestselling novelist or an accountant?

Many of the "facts" about Kelly in that story might lead you to answer that she is a novelist. Only one--her sense of organization--might have pointed you toward an accountant. But think about the overall probability of each career. Very few bookworms become successful novelists, and there are many more accountants than (successful) authors in the modern workforce. Conditioning on the baseline rate helps make a more accurate decision.

I make a similar point--this time applied to political forecasting--in a recent blog post for the blog of Mike Ward's lab (of which I am a member):

One piece of advice that Good Judgment forecasters are often reminded of is to use the baseline rate of an event as a starting point for their forecast. For example, insurgencies are a very rare event on the whole. For the period January, 2001 to August, 2013, insurgencies occurred in less than 10 percent of country-months in the ICEWS data set....

From this baseline, we can then incorporate information about the specific countries at hand and their recent history... Mozambique has not experienced an insurgency for the entire period of the ICEWS dataset. On the other hand, Chad had an insurgency that ended in December, 2003, and another that extended from November, 2005, to April, 2010. For the duration of the ICEWS data set, Chad has experienced an insurgency 59 percent of the time. This suggests that our predicted probability of insurgency in Chad should be higher than for Mozambique.

I started writing that post before rebels in Mozambique broke their treaty with the government. Maybe I spoke too soon, but the larger point is that baselines are the starting point--not the final product--of any successful forecast.

Having more data is useful, as long as it contributes more signal than noise. That's what ICEWS aims to do, and I consider it a useful addition to the toolbox of forecasters participating in the Good Judgment Project. For more on this collaboration, as well as a map of insurgency rates around the globe as measured by ICEWS, see the aforementioned post here.