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Bernie and the Bird, Mar 25 2016

From NPR:

Democratic presidential candidate Bernie Sanders smiles as a bird lands on his podium when he addresses the crowd during a rally at the Moda Center in Portland, Oregon, Friday. At a rally in Portland, Ore., on Friday morning, Bernie Sanders had an unexpected visitor. And the crowd went wild.

Bernie's evident delight, the crowd's roaring applause and the tiny bird's savoir faire were social media gold. The moment prompted a Twitter response that was completely opposed, in content and tone, to, well, the other big political conversation on social media right now.

And nearly a full day after the bird made its appearance, #BirdieSanders is still trending.

Specific Hypothesis and Results

The GCP was also delighted, and set a formal event for the two hour period surrounding the bird's visit. That was about 11:00, so the event ran from 10:00 to 12:00 local time (17:00 to 19:00 GMT). The result is Chisquare 7412.8548 on 7200 df for p = 0.0390 and Z = 1.7621.

Interpretation

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.

bernie.bird.160325=
Cumulative Deviation during event

Standard caveat

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.