There are many things to remember about this week’s presidential election in the US — vast amounts of money, a polarized electorate, an empty chair, and much more. But one lasting change may be the emergence of a truly viable meta-analysis of polling and projections courtesy of Nate Silver and his FiveThirtyEight blog.
But getting the story right is important.
A post-election article by Dan Lyons on ReadWrite yesterday sought to make the case that the accuracy of statistical analyses made by Silver for the US presidential and Senate races is a sign of how “big data” will end the era of mystical predictions:
This is about the triumph of machines and software over gut instinct. The age of voodoo is over. The era of talking about something as a “dark art” is done. In a world with big computers and big data, there are no dark arts.
Lyons goes on to conflate what Silver did with other so-called “big data” triumphs, like when Big Blue defeated Kasparov (Silver covers this in his book, and a software bug might have been more important than any database in upsetting Kasparov). Chess is an interesting game, because it is bounded — it has finite data. Processing speed was responsible as much as data for the triumph of Big Blue — the data were never bigger than the mathematical possibilities of chess. The speed of processing made that finite limit approachable in chess time.
However, Silver’s approach didn’t use big data, but a relatively small, carefully curated data set consisting of a set of polls, and a lot of discipline, as he outlines in his methodology section on the blog. The factors he manages while assembling and analyzing the data include:
- Recency – More recent polls are weighted more heavily
- Sample size – Polls with larger samples receive more weight
- Pollster rating – Pollsters committed to disclosure and transparency standards receive more weight
These results are then adjusted based on a few factors:
- Trendline adjustment – If old polls haven’t been replaced, they are adjusted to reflect the overall trendline
- House effects – Some polls tilt right, some left, and this adjustment mitigates those effects
- Likely voter adjustment – Polls of likely voters are given a lot of credence
There are other steps outlined, which you can explore further if you’d like, but the two most important are the least mathematical, yet they are vital to the integrity of the process — Silver believes in publishing and standing behind his numbers, because the process of preparing for publication and anticipating criticism helps to ensure better analysis.
To underscore the relatively limited size of the possible data set, Silver tracked one presidential race and at most 100 Senate races, and various national and state-level polls. That’s not big enough to qualify as “big data,” which is defined as data sets that are:
. . . so large and complex that it becomes difficult to process using on-hand database management tool.
There’s even physical evidence that Silver’s not dealing with “big data” — the FiveThirtyEight forecasts were updating on his laptop in the Green Room as Silver was interviewed on Monday night’s The Colbert Report.
Silver himself is skeptical of “big data.” In his book, “The Signal and the Noise: Why So Many Predictions Fail, but Some Don’t,” which I reviewed last month, Silver writes:
. . . our predictions may be more prone to failure in the era of Big Data. As there is an exponential increase in the amount of available information, there is likewise an exponential increase in the number of hypotheses to investigate. . . . there isn’t any more truth in the world than there was before the Internet or the printing press. Most of the data is just noise, as most of the universe is filled with empty space.
Lyons is comparing Silver’s level-headed approach with a notoriously non-level-headed approach — namely, pundits. Most pundits spout statistics but don’t understand the field of statistics or how to practice it. They are entertainers, not analysts. Therefore, they are held to a completely different standard — ratings, not accuracy. If there were accountable for accuracy, they would all be fired tomorrow, because when it comes to accuracy, they can’t beat the flip of a coin. And they definitely weren’t accurate in their assessments of Silver.
Lyons nails one aspect of the aftermath of the election:
Silver has exposed [pundits] for what they are, which is propagandists and entertainers. And that’s fine. We still need entertainers. Computers haven’t learned to do that yet.
However, the New York Times found that Silver’s FiveThirtyEight blog did prove entertaining to many people, driving up traffic as the election approached:
Small data, careful curation, astute and recursive analysis, public accountability, the nerve to place bets and stand behind your projections and data — those are the things that make for good and reliable analysis. It’s not big data. It’s the audacity of competence.