Yet another solid statistical indicator of election fraud

If you ever took statistics you may remember a thing called "standard deviation."  It's one of the core concepts of stat.  If you have a large set of data, SD tells how likely it is that a given value will occur *normally*--i.e. honestly.  And the more datapoints, the more accurate the SD calculation.

The concept is at least a century old and is universally accepted by rational people who understand math.  It says that in a normal distribution, 68.27 percent of all the measured values will be within one SD either side of the mean.

Over 95% of the measured values fall within two standard deviations either side of the mean.

Barely one-eighth of one percent of the measured values will be more than three standard deviations above the mean.  So if someone claims a measured value is more than 3 SD's above the mean, the odds are roughly 800 to 1 against that being a valid data point--i.e. the odds are it's either a mistake or dishonest.

So if you have a huge database of, say, the percentage of voter turnout for the past 60 years, and the OFFICIAL reported results claim voter turnout in the 2020 presidential election in one state was a whopping 5.5 standard deviations higher than the historical average, how likely is that to be true?  Or to turn it around, how likely is it to indicate huge fraud? 

The odds that the claimed turnout is bullshit are almost 53,000,000 to one.  And yet the Democrats and the mainstream media expect you to accept that the official, reported voter turnout in Wisconsin is honest, not proof that hundreds of thousands of ballots were fraudulent.

Dems and their Mainstream Media allies claim there is absolutely no evidence of fraud.  They'll ignore the point made in this post.  Cuz...orange man bad!