#### Eric Newhill

**New**

OK,

So they questioned the villagers - both intervention villages and control villages - about their symptoms, if any.

13,893 control villagers reported symptoms (8.6%) and 13,273 intervention villagers reported symptoms (7.6%). Total of 27,166 with symptoms in both mask and no mask groups.

Of those reporting symptoms (27,166) 40.3% agreed to blood tests - that's 40.3 % of 27,166 = 10,948 agreed to blood tests.

Here is where I run into a barrier. .76% in control villages and .68% of experimental villages who had their blood tested showed signs of covid infection. That's where Alex's figures are coming from. It's right there.

So now we have 10,948 blood tests, but it doesn't give us the break out. Of the 10,948, how many were control group (non intervention villages) and how many experimental group (intervention villages)?

If you do not have the breakout - the denominator - you can't do the chi-squared or other calculations for statistical significance. .76% of what portion of the 10,948? .68% of what portion of 10,948? What are they hiding?

They should be telling us that of the 10,948 blood tested people - making up numbers for illustration - 6,500 were control villagers wearing and 4,448 intervention villagers. Then give us the numbers of serum positives for each group (or we could just derive the number of serum positive using the .76% and .68%

The denominator is key to performing the test. The math doesn't work without it. Is it .76% of 7,000 or of 8 or of 91? Who the hell knows? And why don't we know? Sample size is critical to calculating margin of error, variance, etc. in the statistics test.

Though that statistical/design approach has issues - like the blood tested were not randomly sampled (they are the subset/people who consented).

It also does not take into account asymptomatic covid (you only got selected to be blood tested if you self-reported symptoms). There are other issues.

The researchers attempt to hide their problems by then extrapolating back from those %s (.68 and .76) in the blood tested group to the entire population using some murky regression analysis. Then they claim statistical significance. This would never pass peer review, IMO. It's pure crap.

There is no test of statistical significance that can be applied to the blood tested group (Alex's favorite figures) because they do not supply a key figure (see above). Which is something that I have been saying in a round about way since the beginning of my entry into this discussion. Ellis be damned.

And that's the name of that tune.

*"Among the 335,382 participants who completed symptom sur- veys, 27,166 (8.1%) reported experiencing COVID-like illnesses during the study period. More participants in the control villages reported incident COVID-like illnesses (n=13,893, 8.6%) com- pared with participants in the intervention villages (n=13,273, 7.6%). Over one-third (40.3%) of symptomatic participants agreed to blood collection. Omitting symptomatic participants who did not consent to blood collection, symptomatic seroprevalence was 0.76% in control villages and 0.68% in the intervention villages. Because these numbers omit non-consenters, it is likely that the true rates of symptomatic seroprevalence are substantially higher (perhaps by 2.5 times, if non-consenters have similar seroprevalence to consenters)."*So they questioned the villagers - both intervention villages and control villages - about their symptoms, if any.

13,893 control villagers reported symptoms (8.6%) and 13,273 intervention villagers reported symptoms (7.6%). Total of 27,166 with symptoms in both mask and no mask groups.

Of those reporting symptoms (27,166) 40.3% agreed to blood tests - that's 40.3 % of 27,166 = 10,948 agreed to blood tests.

Here is where I run into a barrier. .76% in control villages and .68% of experimental villages who had their blood tested showed signs of covid infection. That's where Alex's figures are coming from. It's right there.

So now we have 10,948 blood tests, but it doesn't give us the break out. Of the 10,948, how many were control group (non intervention villages) and how many experimental group (intervention villages)?

If you do not have the breakout - the denominator - you can't do the chi-squared or other calculations for statistical significance. .76% of what portion of the 10,948? .68% of what portion of 10,948? What are they hiding?

They should be telling us that of the 10,948 blood tested people - making up numbers for illustration - 6,500 were control villagers wearing and 4,448 intervention villagers. Then give us the numbers of serum positives for each group (or we could just derive the number of serum positive using the .76% and .68%

*if we had the denominator, which we don't)*. Then we can run some basic statistics to test for significance.The denominator is key to performing the test. The math doesn't work without it. Is it .76% of 7,000 or of 8 or of 91? Who the hell knows? And why don't we know? Sample size is critical to calculating margin of error, variance, etc. in the statistics test.

Though that statistical/design approach has issues - like the blood tested were not randomly sampled (they are the subset/people who consented).

It also does not take into account asymptomatic covid (you only got selected to be blood tested if you self-reported symptoms). There are other issues.

The researchers attempt to hide their problems by then extrapolating back from those %s (.68 and .76) in the blood tested group to the entire population using some murky regression analysis. Then they claim statistical significance. This would never pass peer review, IMO. It's pure crap.

There is no test of statistical significance that can be applied to the blood tested group (Alex's favorite figures) because they do not supply a key figure (see above). Which is something that I have been saying in a round about way since the beginning of my entry into this discussion. Ellis be damned.

And that's the name of that tune.

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