Trump supporters claim that all the election-related court actions were rejected ‘for standing’, without evidence being considered. This is not true. Whilst standing was a big factor, lack of merit in Trump’s claims was mentioned by many judges as well. So let’s confirm this.
My approach was simple: I looked up some judgements and looked up the word ‘merit’ in them. Then I put in screenshots in context, and added a few other screenshots that talked about merit without mentioning the word itself, or that mentioned other issues of factual relevance.
Court: UNITED STATES DISTRICT COURT FOR THE DISTRICT…
Just before the supposed ‘showdown’ on January 6th, the Trump campaign’s ‘data arm’ — the hilariously named ‘Data Integrity Group’ came up with yet another claim. Supposedly, the voting machines ‘subtracted’ votes from Trump. Here is one of their videos — about Pennsylvania (an earlier one about Georgia, alleging the same was doing the rounds a few days earlier).
With a catchy title of ‘423k votes disappear, there are two data-related claims made if you strip away the rhetoric.
Claim 1 — Biden got about 90% of the vote in a number of precincts and this is suspicious. This…
Here, I will give some links to fact-checking websites and ‘respectable’ media articles, addressing some other claims about the election. This page will keep being updated.
In part 1, we considered allegations of vote-fixing in suburban — including deeply republican — counties. Biden’s under-performance in the metropolitan areas such as Philadelphia compared to his over-performance in these suburban and exurban areas was considered to be evidence of fraud. The argument was that the heavily Democrat metropolitan area contained the ‘actual’ Biden performance whilst the Republican outlying areas in which Biden made gains had their performance ‘fixed’ somehow.
One feature of the claims of Trump’s supporters is their scattergun approach — they pump out claims that are mutually contradictory. In a paper by John R. Lott, the…
A more interesting — and thus far not debunked — analysis concerns the difference between mail-in and in-person votes in Pennsylvania. Apart form Philadelphia, it is remarkably consistent — around 40%. This is the image in question — doing the rounds on Trump-supporting websites.
I have checked some of the data points at random, and they seem to be accurate. I do not have an explanation for this at the moment.
In the US, it is common to analyse election results at county level and compare with past elections to see how support for the two main parties changes with time. In 2020, it also seems to give rise to people scouring the county-level data in the ‘battleground’ states — the ones whose results were contested by Trump — to see if they can find a few ‘abnormal’ results in which they can claim election fraud to have taken place. For instance consider this analysis by Scott Hounsell — one of a three-part piece covering Pennsylvania, Michigan and Wisconsin.
Hidden in point 11 of the so-called Ramsland Affidavit is a claim about fractional votes. People noticed that the ‘running total’ percentage share of the vote in the output from Dominion’s voting machines was given as a fraction of 1 — standard practice when writing computer code. They used this to allege that the system was ‘weighing’ votes of a particular candidate — multiplying the vote totals by a weight to ensure a Biden win without being detected.
Ramsland mentions that this is evidence of ‘ranked choice voting’ and refers us to the voting machine guide section 11.2.2. That does…
Assuming statistical independence for events that are anything but independent is a staple diet of election-time fake news. The best example is this claim — made in court — that I reproduce in its full glory:
The ‘argument’ is this: assume each vote has a fixed probability of going to Biden or Trump. What is the chance of Trump being as far ahead as he was in Georgia, Michigan, Pennsylvania and Wisconsin at some point in the count and still going on to lose? Unsurprisingly, under the assumption made these are pretty small. …
When data is manipulated using an algorithm, this can sometimes leave artefacts — tell-tale ‘footprints’ of the processing algorithm. For instance, if we take the modulus of the data (that is, remove the negative signs of the values), we will have a very sharp and distinct boundary at zero. This ‘sharp boundary’ could tell us that something that shouldn’t have been done was done with the data — or alternatively it could be the result of something entirely legit. We would need to dig deeper to find out. Other artefacts are created by linear interpolation, rounding up or correlations.
A particularly bold claim about voting machines surfaced a few days ago. An anonymous account on Rumble claimed they built a predictive model. This predictive model took every one of the of the 3243 US counties and county-equivalents in the US and compared the Biden vote share predicted by the model with the actual result.