Survey Results: Where Are All the Sick People? — Watts Up With That?

The “Where Are All the Sick People?” survey has had nearly 3000 participants since its inception at 10 a.m. EST. Three questions were posed to illuminate the issue of the effects of the SARS-CoV-2, which is causing the current Covid-19 Pandemic, on the readers of this blog, WUWT.

Survey Results: Where Are All the Sick People? — Watts Up With That?


  1. If you don’t know any people sick with/from Covid-19 you having the same Covid-19 experience as the vast majority of other people – at least according to this somewhat unscientific survey.
  2. If you don’t know anyone who has died, or only one or maybe two, you are again having the same experience as almost everyone else.
  3. While most of us don’t know anyone who has died from/with Covid-19, we probably know someone who does know someone who has sadly lost a family member or acquaintance during the ongoing pandemic.
  4. Opinions vary wildly on the subject of Governmental Responses to the pandemic.  It will be years before the historians, sociologists, medial researchers, and others sort out the quagmire of mistakes that have been made at all levels of governance.

(Chi) Squaring up Benford’s Law

You may have heard about Benford’s Law. You may have seen the charts. You may have seen the video where the charts are featured. Links to the datasets are included in the description of the video.

I was finally curious enough to look at the data and run a chi-squared analysis of the data. The idea here is that I can compare the observed number of times a given number is the first digit in all the precinct-level counts, and the expected number according to Benford’s Law.

The first attempt yielded huge values, corresponding to wildly improbable differences between the observed first digits of the precinct counts and the values expected according to Benford’s Law. The smallest chi-squared statistic, corresponding to the closest fit with Benford’s Law, was still quite improbable — less than one chance in a billion of being due to chance. The largest ones had a likelihood similar to winning the Powerball grand prize a dozen times in a row.

On further reading, I learned that the Chi-square test is rather sensitive to sample size, even though the sample size does not show up in the formula. Large sample sizes will result in chi-square statistics that look highly significant (highly unlikely) even for small deviations from the expected value.

So I redid the calculation after dividing all of the values for each category by a number intended to make the smallest value in all the categories equal to five.

Why five?

The chi-square test starts to run into trouble when the value of any category drops below five. For a large number of categories, it’s OK if 80% of the values are five or greater. That would mean I could have set it so the second-smallest value is five. However, setting the lowest value at five gave me reasonable results.

I looked at the data for Allegheny County, PA and Fulton County, GA.

Allegheny County, PAFulton County, GA
Trump: total votesX2 = 5.80p = 0.669X2 = 4.00p = 0.857
Biden: total votesX2 = 190.5p = 5.73e-37X2 = 15.50p = 0.050
Apparent shenanigans in Biden’s vote totals in Allegheny County

In both counties, Trump’s precinct-level vote totals match pretty well with Benford’s Law. In Fulton county, Biden’s vote totals are on the edge of significance.
In Allegheny county, Biden’s vote totals vary from Benford’s law by an amount well outside the bounds of chance.

Can we call “shenanigans” here?

Media Hoaxes: No, Sturgis Was Not A ‘Superspreader Event,” And No, It Did Not Cost ‘Public Health $12.2 Billion’

Gov. Kristi Noem: “This report isn’t science; it’s fiction. Under the guise of academic research, this report is nothing short of an attack on those who exercised their personal freedom to attend Sturgis” The post Media Hoaxes: No, Sturgis Was Not A ‘Superspreader Event,” And No, It Did Not Cost ‘Public Health .2 Billion’ first appeared on Le·gal In·sur·rec·tion .

Source: Media Hoaxes: No, Sturgis Was Not A ‘Superspreader Event,” And No, It Did Not Cost ‘Public Health $12.2 Billion’

The Pandemic is Winding Down

This is a nice, well-sourced piece in The American Thinker

In contrast to the Covid19 attributed deaths, the number for deaths from all causes is a hard number.  The deaths from all causes number exposes the current mass panic as an historical aberration and confirms the evidence that the mass panic has been engineered by politicians and a biased medical establishment.

The Covid-19 Pandemic is Ending

The 11 year weekly deaths from all causes graph (here and below,) shows that the 2020 flu season was about normal until it spiked for eight weeks in April and May due to Covid19 (CDC data here and here).  The April high of 78,000 was significantly higher than the previous multi-year high of 67,000 in 2018, but just as in previous years, the temporary spike rapidly declined toward baseline.

The 11 year baseline increased from about 45,000 in 2009 to about 51,000 in 2019, generally as a result of increasing population.  After this year’s April spike to 78,000, the current weekly all causes deaths number is down to 55,000, about 4,000 higher than the projected baseline.  We are at week 32 of the year, and flu season is about to kick in.  What does this mean regarding the Covid19 epidemic?

Testing Targets and Intensifies Social Distancing on the Infectious

I’ve been pounding on the need for fast, frequent testing but it’s clear from some of the comments to The Beginning of the End that I have failed to convey some fundamental points. A seemingly sophisticated objection is to note that given background prevalence rates even a fairly specific test will result in a high fraction of false positives among those who test positive. (This is the standard Bayesian doctor puzzle .) It’s nice to see people doing the Bayes calculation but some of them are then drawing the wrong conclusion.

Source: Testing Targets and Intensifies Social Distancing on the Infectious