Shout out to the people who are trying to figure this one out.
If you want to be on the safe side, it’s important to know that the numbers in the tweet above are the ones most likely to be true.
If they’re not, the odds are good you’ve been cheated by someone you don’t like.
In the case of the Democrats, the best guess is that the majority of the tweet’s respondents were Democrats, but not everyone in the data set is.
So if you want a solid estimate of the true extent of the party’s vote loss, we can give you some clues.
This chart plots how the percentages of Democrats and Republicans are distributed across each candidate in the last 100 days.
If Trump has won the presidency by only 1 percentage point, his margin of victory is 5 percentage points.
If Hillary Clinton has lost the presidency, her margin of defeat is 11 percentage points, or 5 percentage point.
This suggests that Clinton’s victory was the largest among the candidates, but it’s not clear whether that’s true of Trump.
A third way of looking at the numbers is to use a mathematical model.
We’ll start with the easiest: the mean.
This tells us how the probability of a candidate winning is proportional to the number of candidates in the contest.
So, for example, if Trump has a 50% chance of winning the presidency (the median), his chances of winning are 25%.
If Clinton has a 25% chance (the mean), her chances of losing are 17%.
A more complex way of doing it is by considering the variance of each candidate’s probability.
The more the candidates’ shares fluctuate over time, the more likely it is that they’re in a contest with a small sample size.
For example, in the 2016 election, Clinton’s share of the vote was just over 25%.
Trump’s share was around 40%.
We can think of the variance in the mean as a measure of how close each candidate is to the mean and how far they’re from it.
A high variance means that Trump is winning more than one-in-four of the contests.
A low variance means he’s winning fewer than one in four of the races.
The variance of the mean means is a way to calculate how likely it would be for the candidate with a high variance to win the election.
In a sense, a candidate’s vote is a measure.
In an election like this, the probability that a candidate with high variance would win is the same as the probability they’d lose.
For this reason, the mean of a campaign is often used as a guide to how a candidate will perform.
And as we’ve seen, Trump is in the middle of that range.
The mean of the past 100 days is about 1.3 percentage points lower than the mean that Hillary Clinton would have received in the same time period.
The best guess for the true probability of Clinton winning the election is a little higher, at 1.9%.
That means Trump’s win is about 50 percent less likely than Clinton’s.
That means his margin is about 9 percentage points less than Clinton, but he’s still likely to win.
In other words, Trump will still win the presidency if we simply look at the variance instead of the probability.
So the question is whether Trump will be as good a president as he’s shown he can be.
His odds of winning, according to the data, are just a hair better than the one we’d expect.
If that’s the case, the Trump-Clinton race will continue to resemble a close race, with the probability remaining roughly the same.