RebeccaGoldin24 karma2016-11-21 18:08:24 UTC
I'll give you a binomial distribution with p=0.0.
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RebeccaGoldin21 karma2016-11-21 17:40:35 UTC
Perhaps our brains are hardwired to believe there's a reason behind everything -- and we jump to the most obvious conclusions. You may enjoy this site with a lot of spurious relationships.
RebeccaGoldin19 karma2016-11-21 17:38:25 UTC
The biggest abuse is, in its own way, the easiest to disentangle. It's the implication of causality when a correlation is found.
RebeccaGoldin15 karma2016-11-21 17:46:54 UTC
Systematic bias could definitely create the outcome we saw. Systematic bias would include anything that unexpectedly favored recording the opinions of Clinton supporters over Trump supporters.
Another factor may have been the estimates about who would actually vote. Pollsters had to predict who would actually show up to vote. If they (as a whole) over-estimated the likelihood of Clinton supporters voting, and under-estimated the likelihood of Trump supporters, we would also see this effect!
Finally, another contributing factor pertains to when people made their decisions. Earlier on in the election cycle, people who didn't like either candidate stated that they would not vote. Perhaps more of these people decided to vote, and vote for Trump, than expected.
RebeccaGoldin15 karma2016-11-21 17:32:30 UTC
With jokes is the best way! A great example would be to explain the effect of confounding factors on polls (or data collection generally). Here's a poll pointing to Democrat vs. Republican experiences with sex
I bet they didn't control for the confounder of gender: women are more likely to be Democrats.
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