Back in 2000 and again in 2004, I enjoyed a small piece of
influence through political opinion poll analysis. Statistics is an intriguing science, all the
more because it tries to quantify and predict human behavior. But that same human behavior also skews how
people think, including analysts, and in 2008 and 2012 it caused me to miss important
trends in American politics. I was
embarrassingly wrong in predicting the Presidential elections, especially
missing the energy of Obama’s 2008 run.
So I backed off, paid more attention to my regular job and family, and
paid less attention to statistics.
Others enjoyed the attention of poll mavens, especially Nate Silver, who
turned his statistical devotion to baseball into political success with Obama’s
success. But Silver made the same
mistake I did, and in his case the embarrassment is greater because as a
professional statistician, he really ought to have known better. Silver let his enthusiasm for Democrat
opinion cause him to ignore warning signs until it was too late to avoid a face
plant.
Let’s have a quick review of how polls saw the 2016
Presidential Election, and also how polls work, and finally how predictive
analysis is created.
Hillary Clinton announced her decision to run for the White
House on April 12, 2015. This is
important because Clinton already enjoyed significant name recognition and with
the roles of First Lady, Senator and Secretary of State on her resume, she
would start as an obvious front-runner for the Democrats’ nomination. Nate Silver gave her a 59.9% chance of
winning the party nomination at the beginning (I’m using Silver here for two
reasons – first, his projections are built from aggregates of major national
polls, and second, Silver was the most prominent poll analyst quoted in the
media). She enjoyed media support
through the end of 2015 as the presumptive front-runner, but by the end of October
2015 Clinton’s lead over Sanders in Silver’s chart was down to 46.8% to 26.1%,
notable not for Sanders’ strength but Hillary’s weakness. By February 2016, Silver put the race at
49.6% Clinton to 39.1% Sanders – note that Hillary’s campaign was failing to
win over most of the undecideds, losing them to Sanders more than four to
one. By April 23, 2016 Silver had the
race 49.6% Clinton to 41.5% Sanders; note two important factors apparent, first
that Hillary appeared to have a lead bigger than Sanders could close, but
second that Sanders had more momentum than Clinton, and had enjoyed higher
energy for some months. By the end of
June, Silver showed the race 55.4% Clinton to 36.5% Sanders, essentially a done
deal for the Democratic Party nomination.
http://projects.fivethirtyeight.com/election-2016/national-primary-polls/democratic/
Donald Trump announced his candidacy for the office of the
President on June 16, 2015. At that time
Silver counted his support at a 3.6% chance of winning the GOP nomination. Let’s stop there and consider that this meant
the polls showed Hillary Clinton’s chances of winning her party’s nomination
were more than sixteen times greater than Donald Trump’s chances of winning his
party’s nomination. Part of this was due
to the heavy number of candidates for the Republican nod, but also Donald Trump
– while known as a face and name – was unknown as a political contender, so he
had to establish his bonafides with both the GOP and the voters. Trump’s campaign quickly gained support,
however, as he passed the 20% threshold on July 26, 2015, and the 40% threshold
on March 21, 2016. This means that
Donald Trump had not won over most voters until after his Super Tuesday wins in
Alabama, Arkansas, Georgia, Massachusetts, Tennessee, Virginia and
Vermont. On March 22, Trump claimed
another 58 delegates by winning the Arizona primary. By the end of May, Trump had essentially
locked up the GOP nomination.
http://projects.fivethirtyeight.com/election-2016/national-primary-polls/republican/
Both Clinton and Trump finished the win-the-nomination part
of their campaigns with damage, however.
Trump’s problems were obvious – to energize his base, Trump attacked
establishment Republicans and demographics aligned with opponents of populist theory,
and this cost him nationally in polls. In early June, polls showed Trump’s
support at 38.1%, compared to 42.1% for Clinton. But Clinton had obvious problems, too. The way Clinton won the Democrats’ nomination
left many Sanders supporters convinced the primary had been rigged, which may
be one reason Trump made similar claims as the General Election reached its
resolution. But also, given the many
demographic groups Trump had – allegedly – attacked, a four-point lead for
Clinton was a clear warning sign that something was not as described.
Call it a poll version of that annoying “check engine” light
on your dashboard. Until you have
someone get under the hood, you don’t know what exactly has gone wrong, but you
can’t ignore it unless you don’t mind spending hours on the side of the road
beside your smoking vehicle, at the mercy of passing traffic. There is science behind a poll that is put
together and analyzed properly, but laziness or assumptions in your data or
procedures can invalidate your conclusions, and make you look a fool in public.
By the way, Nate Silver uses an aggregate of polls, but he
is also guilty of some subjectivity in his source selection. For example, Silver’s aggregate shows Clinton
had a wire-to-wire lead over Trump in polling, with Trump never enjoying a lead
in the aggregate polling at any time:
http://projects.fivethirtyeight.com/2016-election-forecast/national-polls/
Real Clear Politics,
however, which also uses an aggregate of polls, showed Donald Trump with an
aggregate lead on May 24 and from July 25 through July 28 of this year.
http://www.realclearpolitics.com/epolls/2016/president/us/general_election_trump_vs_clinton-5491.html
That’s not to say one aggregate is ‘better’ than the other,
but to illustrate the fact that any aggregate is subjective and contains
implicit bias. Ironically, Silver was aware of this bias and tried to correct
for it – he calls this “trend line adjustment” – but in the end Silver’s own
bias still influenced his conclusions.
http://www.huffingtonpost.com/entry/nate-silver-election-forecast_us_581e1c33e4b0d9ce6fbc6f7f
It’s important to remember that Silver was wrong about Trump
winning the GOP nomination. After trump
won the GOP nomination, Silver admitted “we basically got the Republican race
wrong.”
http://fivethirtyeight.com/features/why-republican-voters-decided-on-trump/
There was no evidence that Silver went back to find the evidence
he overlooked in his initial analyses, which could have corrected his results
in the General Campaign. But here is, at
least, evidence that Silver knew something in the numbers was wrong. Just before the final day of the election,
Silver put out his “final election update”, giving Clinton a 71% chance of
winning.
http://fivethirtyeight.com/features/final-election-update-theres-a-wide-range-of-outcomes-and-most-of-them-come-up-clinton/?ex_cid=2016-forecast
This ran contrary to far more aggressive posts from the New
York Times, which gave Clinton an 82% probability of winning,
http://www.nytimes.com/elections/forecast/president
the Princeton Election Consortium gave Clinton a 93% chance
to win the White House,
http://election.princeton.edu/2016/11/08/final-mode-projections-clinton-323-ev-51-di-senate-seats-gop-house/
left-leaning pundit Larry Sabato did not offer a
probability, but called for Clinton to win 347 Electoral Votes,
http://ijr.com/2016/08/667335-famed-election-predictor-with-97-100-track-record-reveals-his-trump-vs-hillary-2016-results/
and of course the Huffington Post posted that Clinton had a
98% chance to win the Oval Office.
http://elections.huffingtonpost.com/2016/forecast/president
Anyone who turned on ABC, NBC, CBS, CNN, or Fox was also
flooded with assurances that Clinton was poised to win by large margins. That all of these analysts were wrong, and
to such a large degree, is amusing given their hubris, but concerning given
their prominence in media coverage of the election.
The last week of the election, Nate Silver’s concerns about
the polling data caused him to scale back his probability for Clinton (he
initially had Clinton at 89%, but as the election approached he walked it back
to 71%), while Ryan Grim of the Huffington Post kept Clinton at a 98% chance to
win. This led to some ill-advised words on Twitter between the two men about
each other’s methodology.
http://www.vox.com/2016/11/6/13542328/nate-silver-huffpo-polls
Ironically, while Silver was correct that weighting
Clinton’s advantage beyond anything supported by poll data was foolish, he
failed to properly test the underlying assumptions installed in his own model.
I found it intriguing to notice that neither Gallup nor Pew
published polls for the Presidential election, each focusing instead on issues
rather than candidates. A business
reason was provided,
http://time.com/4067019/gallup-horse-race-polling/
but given the long history and prominence Gallup and Pew
enjoyed in polling Presidential races,
the reason given rings false. A more
likely explanation is the difficulty in addressing behavior changes in the
voting public. In addition to the shift
from landline phones to cell phones, voters are more likely to discuss opinions
on line than in a phone interview, but there is no statistically sound means to
randomly contact respondents online and the results of online polls are as
varied as there are opinions reported by them.
Pew observed that online polls are “non-probability” polls, which
eliminates by definition the random nature of polls, and therefore calls into
question any political conclusion presented by such a poll.
http://www.pewresearch.org/fact-tank/2014/07/28/qa-what-the-new-york-times-polling-decision-means/
Pew also posted an article yesterday about why the polls
were essentially wrong, but was wrong to pretend weighting mistakes were not a
big part of blunder.
http://www.pewresearch.org/fact-tank/2016/11/09/why-2016-election-polls-missed-their-mark/
Forbes boasted that analysts predicting a Hillary win “used
the most advanced aggregating and analytical modeling techniques available”
http://www.forbes.com/sites/startswithabang/2016/11/09/the-science-of-error-how-polling-botched-the-2016-election/#4d6c04257da8
but that is a false claim on its face. What happened was not a “statistical error”,
but human error. Weighting for party
affiliation or other demographics, is risky at best and often leads to
unreliable results. To see what I mean,
let’s start with the exit poll from the 2012 Presidential Election, by party
affiliation, gender, race, and age:
Party Affiliation: Democrats 38%, Republicans 32%,
Independents 29%
Gender: Women 53%, Men 47%
Race: White 72%, African American 13%, Hispanic 10%, Asian
3%, Other 2%
Age: 45-64 38%, 30-44 27%, 18-29 19%, 65 & over 16%
http://ropercenter.cornell.edu/polls/us-elections/how-groups-voted/how-groups-voted-2012/
And from 1984 through 2014:
Party Affiliation: Democrats 38.6%, Republicans 32.6%,
Independents 27.5%
Gender: Women 53%, Men 47%
Race: White 76%, African American 13%, Hispanic 7%, Asian
2%, Other 1%
Age: 45-64 33%, 30-44 28%, 18-29 14%, 65 & over 25%
http://www.electproject.org/home/voter-turnout/demographics
http://ropercenter.cornell.edu/polls/us-elections/how-groups-voted/
Any poll with demographics different from these numbers is
fiddling with the numbers out of clear bias.
Without wasting time going through them this skewing invalidates polls
from ABC News, the Wall Street Journal, Fox News, NBC News, CNN, and CBS. If you want to check for yourself, simply
find one of their polls and drill down to the demographics which are usually
included at the end of the topline detail.
Weighting is not supposed to produce the “right” answer, but
to line information up according to known population demographics. Sadly, a lot of polls screw up the results by
trying to sell a message, rather than accurately report the current situation. This is not an attempt to “rig” an election,
I believe, but simple human laziness and a habit of using assumptions instead
of due diligence.
This becomes ever more salient, when you realize that the
aggregates used by analysts like Silver and Grim incorporate these biased
reports, which invalidates their own analyses.
Aggregation is really just group-think, even if some people publish such
results with impressive names like “meta-sampling”. Everything that goes into an analysis should
be tested for its own veracity, and while this is very difficult for a national
report, at the very least you should be candid if you are trusting someone
else’s report as a source for your own analysis. Yes, Silver claims he ‘unskews’ polls by
other agencies, but that’s kind of like a guy admitting someone spit into your
drink but he scooped it out and it’s fine for you to drink. If you know the source is biased, it does not
belong in your own work, none of it.
One last thought on polling.
The Presidential Election is not a national race, no matter what the
media tells you. It’s actually fifty-one
different races, which results are summed up and produce the champion, in this
case the President-Elect of the United States.
So the polls you ought to have watched are the state polls, especially
according to the respective electoral vote value of each state. Most media ignored the state-level polling,
and when it was reported it was usually just from a single source that the
media found reliable. I will be publishing
a report on the accuracy of the state polls for the 2016 Election when I have
all the data, but for now it’s important to know the limits of what analysts
even can tell you, and keep in mind that most media people are there to sell
you entertainment, not facts.