[This article was first published on Behavioral Security » rstats, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

The headlines have been buzzing with the “triumph” of statistics and math in this election.  But before I jump into how well statistics served us, let’s do a little primer on the margin of error.

Whenever we measure less than the whole population we’ll have some variability in the sample.  Chances are good that the sample will not precisely match the entire population.  However, we can do some calculations to estimate our confidence in the true parameter and that’s called the margin of error.  Most polls and surveys will use a 95% confidence interval to calculate the margin of error.  It’s important to know that this isn’t a 95% probability, it is a confidence interval.  That is 95% of the time, we expect the true value to be within the margin of error (and yes that’s different then saying the true value has a 95% chance to be within the margin of error).  In other words, if we make one hundred predictions with 95% confidence, we would expect that the true value of 5 of those (5%) would be outside our margin of error.

Now we get to Nate Silver’s work.  We cannot judge his work by how many states “right” or “wrong” he actually got (though many people are).  Instead we have to judge by the accuracy of the forecasts within the confidence intervals.  He made 51 forecasts of how the states (and D.C.) would turn out with 95% confidence.  Therefore, we cannot expect him to be right 100% of the time, only 95% of the time.  If he were right 100% of the time, then there’s something wrong.  Instead we would expect (51 forecasts * 5% wrong = ) 2.55 states should be outside of his confidence intervals, or in other words, 2 or 3 election results should fall outside the confidence intervals.   But even getting 1 or 4 states may still be expected.  However, it’d be worthy of raising an eyebrow if all 51 forecasts fell within the margin of error.

We should expect 2 or 3 states to have election results that fall outside the margin of error.  The forecasts would be more inaccurate if all 51 fell within the margin of error.

### So how’d he do?

Pulling data from Nate’s blog (he lists all 51 forecasts on the right side), I was able to make a list.  For example, in Alabama, he listed Obama as getting 36.7% of the vote and Romney getting 62.8% with a margin of error of 3.8%.  Which means, come election day we expect Obama to get between 32.9%-40.5% of the vote and Romney should get between 59%-66.6 (with 95% confidence).

Next we pull the actual results.  I grabbed data from uselections.org and sure enough in Alabama, Obama recieved 38.56% of the vote and Romney got 60.52%.  Both fall within the margin of error, congratulations statistics.

When it’s all said and done, Nate Silver correctly forecasted 48 of the 51 election results and that’s great!  We expected 2.5 states to be outside the margin of error and 3 were.  He could not have been more accurate.  If he had gotten 51 of 51 states correct, the forecasts would be more wrong because these are estimates with 95% confidence.

### What’s that look like?

Combining the two data sources (forecasts with results) we can see that the three states that fell outside the margin of error were Connecticut, Hawaii and West Virginia (marked with asterisks).  But looking down the list, we can see how varied the forecasts were and yet how specific they were and how well almost all of them did.   Another interesting thing to pick out: in states that were not contested, the margin of error was much larger (since fewer polls were done in those states) so the lines were longer, and in the swing states that margin was considerably smaller.   (click image to enlarge)

For those interested, this visualization was done with R and code and source data are both located on github.

To leave a comment for the author, please follow the link and comment on their blog: Behavioral Security » rstats.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

# Never miss an update! Subscribe to R-bloggers to receive e-mails with the latest R posts.(You will not see this message again.)

Click here to close (This popup will not appear again)