And now to US
1. And now to US
The goodness of fit test that you just conducted used as the null hypothesis that the first digits in the voting data were drawn from the digit distribution specified by Benford's law.2. Iran election fraud
This corresponds to the notion that the 2009 presidential election in Iran was fair.3. Iran election fraud
The alternative hypothesis was that the election was fraudulent, that Benford's law does not describe this voting data well.4. Iran election fraud
Our results are well summarized by looking at the null distribution of the chi squared statistic either under a simulation or using the mathematical approximation. The two methods give a very similar answer, that the chi squared distance that we actually observed indicated by the vertical line is very unlikely if indeed Benford's law does hold.5. Iran election fraud
If we're comfortable with establishing election fraud by using Benford's law, then this analysis serves as evidence of fraud in the Iranian election, but before you embrace that real life conclusion, let's take a moment and think through a good sanity check. One way to check the viability of assessing electoral fraud using Benford's law is to look to an election that you think to be fair and run the same analysis.6. U.S.A. 2016 election
For that, let's turn to the voting data from the US presidential election of 2016 where the two primary party candidates were Donald Trump and Hilary Clinton. You'll use the same hypotheses and the same analytical approach, extracting first digits and seeing how well they conform to Benford's law. While you could consider this analysis at many different scales, let's focus in on a single state, Iowa.7. Iowa vote totals
In the following exercises, you'll be working with the Iowa dataset.8. Iowa vote totals
In substance, it contains the same vote total information as the Iran dataset, but form is a bit different.9. Let's practice!
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