COVID-19 Info

“While the data is not as scientifically rigorous, the whole-populations studied are likely large enough to minimize confounding factors, and offers no support for a benefit of masks on cases or deaths. Please forgive the passionate presentation, and take the data for what it is worth. No other data set I have seen is larger, and as this analyzes whole populations, likely is as good a data as can be gotten.”


“This study claims to support mask mandates, by demonstrating that there was a statistically-significant reduction in the case development rate in mandate-locales, but an insignificant reduction in non-mandate locales. This is an invalid statistical argument. Just because variable ‘A’ changes significantly over time, and variable ‘B’ changes less significantly, does not mean the two variables are statistically different. This analysis requires that the confidence intervals (error bars) between the two variables don’t overlap. If you look at the two graphs presented, and overlap them, you will see that the confidence intervals overlap, proving that there is no statistically-significant difference between the mandate and non-mandate locales. It therefore, if anything, argues against mandates. This is a rather rudimentary error in statistics. Usually when such data is presented, both variables are presented on the same graph, so that this comparison is easier to see. I cannot explain why the authors made the mistake, and presented the graphical data as they do. Even if this can be seen to argue for mandates, the effect was very transient, and of very small degree.”