US Presidential Election 2016
The most surprising, even shocking US election in many years has just ended with Donald Trump winning over Hillary Clinton. The ugly campaign was accompanied by polling that virtually always showed Clinton with a comfortable lead. But the polls were wrong. The eventual vote counts showed Trump winning states which had been expected to go to Clinton, and by the time Florida results came in after midnight, many observers began thinking an upset was in the offing. Then Pennsylvania, then Wisconsin, both "Blue Wall" states expected to be for Clinton, fell to Trump and the picture became clear. Both CNN and NBC reported that Hillary Clinton called Donald Trump to concede the race at about 02:40 ET.
Shock and sadness was evident in the scenes of Hillary supporters, and jubilation at the victory celebrations for Trump.
Specific Hypothesis and Results
This analysis is, as other recent ones have been, an exploration, but the hypothesis was set exactly as in previous elections. A 24 hour period beginning at 15:00 Eastern Time was predicted to show deviation. The result shown below does not have a long, consistent trend, but does have unusual variation, with a very strong 5-hour positive trend beginning about the time the end result was becoming clear, and an equally notable, 10-hour long negative deviation beginning about the time of Clinton's call to concede the election to Trump. The result is Chisquare 89460 on 89401 df for p = 0.444 and Z = 0.141.
The following graph is a visual display of the statistical result. It shows the second-by-second accumulation of small deviations of the data from what’s expected. Our prediction is that deviations will tend to be positive, and if this is so, the jagged line will tend to go upward. If the endpoint is positive, this is evidence for the general hypothesis and adds to the bottom line. If the endpoint is outside the smooth curve showing 0.05 probability, the deviation is nominally significant. If the trend of the cumulative deviation is downward, this is evidence against the hypothesis, and is subtracted from the bottom line. For more detail on how to interpret the results, see The Science and related pages, as well as the standard caveat below.
It is important to keep in mind that we have only a tiny statistical effect, so that it is always hard to distinguish signal from noise. This means that every
success might be largely driven by chance, and every
null might include a real signal overwhelmed by noise. In the long run, a real effect can be identified only by patiently accumulating replications of similar analyses.