Tuesday, January 26, 2016

Another Podcast

Hi Everyone.

I'm stealing Arielle's idea and sharing this Freakonomics podcast about how to become a "super-forecaster" that aired last week. It's an interesting take on how important understanding probabilities and statistics can be outside our hard-science world.

I particularly appreciate the comments that what sets apart a bad or overconfident "forecaster" from a "superforcaster" is dogmatism. In the context of this podcast, dogmatism is discussed as a personal desire to come up with reasons to support a preferred prediction with a tendency to disregard reasons that go against the preferred prediction. I think this idea can be extrapolated quite well to the scientific community, and community in general, as a whole. Often, what has been done previously or what is discussed with the most passion steers decision making just as much as-- if not more than-- the direction most evidence is pointing.

Listening to this podcast (for the third time, now) reinforced in my mind that we should embrace open mindedness and flexibility while allowing data-- be it in a scientific context or not-- to drive our opinions and change them throughout time.

The podcast closes by campaigning for more accountability in public debate following up on all predictions rather than simply choosing to discuss those that are convenient to discuss at a later date. I completely agree that this strategy could eliminate that tendency of public personas to make sweeping, dramatic promises and predictions that can rile up the populace with little (or no) basis or consequence. If we are going to continue as (or return to) a civil society it is important to remember that thinking and effort are critical components.

1 comment:

  1. Thanks for sharing that podcast, Erica. I found the section that describes the typical characteristcs of "superforcasters" to be particularly telling. They're described as believing in chance, being humble about their judgments, being actively open-minded, and being curious. I think those qualities are also important for scientists, especially when it comes to the statistical analysis of our own data.