Nate Silver – Royal Geographical Society

Nate Silver recently spoke at the Royal Geographic Society, here in London.  He’s possibly the closest thing to real celebrity statistics and modelling currently has, and yet, he was quite humble in presentation.  In fact, he’s the first to admit that his popular 538 Blog’s US election model is based on quite a simple approach:

  1. Track and aggregate daily polls
  2. Weight the polls based on their past performance, and political bias
  3. Establish a margin of error, which shrinks as the election nears

For a statistician, this approach seems obvious, but in the space of political punditry and news media outfits this is revolutionary   Of course, tracking polls, and the poll’s performance requires a higher level of effort and discipline than just merely repeating the daily polls figures.  It’s my hope that the Nate Silver effect, will be on the public’s critical response to polling and political commentary.  In particular, the public’s awareness of margin of error, and perhaps their appetite for news to be accompanied with some discussion of uncertainty about their results.

Of course, the public, like the audience at the RGS, loves their mystical oracles.  And quite a few questions focused on Nate’s opinion ranging from the next big US Election in 2014, to Labour’s performance if the General Election were held today, and to how we can live our everyday life better using statistics and modelling (Nate does not recommend creating “a spreadsheet of kebab shops every time you want to go out for a kebab”).  Nate’s responses were quite even and unsensational, which is quite refreshing to an off-the-cuff ‘expert’.  In fact, his team attempted to track the UK Elections, but things that are super difficult here is the multi-party complexity (just look at the UKIP’s upset this past week), as well as there are just less frequent and common political polling being done.  In light of this, the two-party. poll-rich domain of the US elections look quite simple indeed.

And this brings us to a valuable question to consider in light of Nate’s talks… what makes a fertile domain to easily (and properly) model and explore?

How about these to start:

  1. Rich and open-data environment – baseball is great with a long history of recorded statistics, while football is harder with long fluid game play.
  2. Explicit performance metrics – sports, elections, and gambling have discrete winners and losers, while advertising effectiveness is more wiggly to identify and measure performance
  3. Frequent measurement episodes – multiple daily polls measuring the public’s political opinion is far more frequent domain to study, than say than large-magnitude earthquakes.



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