Your reference is from March 17, 2020, which means that it is grossly outdated.

It doesn't make sense to compare the "crude mortality ratio" (usually called the case fatality rate) for two diseases, because the case fatality rate depends heavily on the proportion of asymptomatic infections and how much testing is taking place. To accurately compare fatality, you need to consider the infection fatality rate, not the case fatality rate. For COVID-19, there are an extremely high number of asymptomatic infections; only about 15% of infections go detected. We now know that the infection fatality rate for COVID-19 is somewhere close to 0.6%, which makes it about 5-10x deadlier than seasonal influenza, although impacts differ by age group.

It also doesn't make sense to say what the R0 for a virus is without context... R0 is not an implicit aspect of the virus; it depends explicitly on population density, control measures, the proportion of the population that is already infected or immune, etc. The entire purpose of lockdown, masking, and all the other control measures was to get the R0 below zero so that infections would decrease. Further, the R0 was probably much higher than was initially suggested because asymptomatic infections were spreading rapidly and went undetected.

The yacht company explicitly says that he is *not* buying it.
https://ancillary-proxy.atarimworker.io?url=https%3A%2F%2Fwww.bbc.com%2Fnews%2Ftechn...

California receives $0.99 in federal expenditures per dollar of taxes paid, which is below the national average return for states of $1.22 per dollar paid

It's a "donor" state, but not by much.

And where did the "government agency" get the money from?
That's right, the taxpayers.

Generally, no. The business is fined a multiple of the wages illegally not paid, and the employee is paid from those.

So, the employee is paid by the employer, and the rest of the fine goes towards funding the government agency.

How exactly does one "plan on going" private in Ontario with health care when providing private health care is essentially illegal?

Private dental, mental, and prescription coverage, but you can go private for many doctors, too.

I use EQ virtual, but there are other apps that can basically do the same thing. It's illegal to see Ontario doctors for a fee, but it's perfectly fine to see doctors from any other province, and for $50 I can be seen in an hour or two. I also see clinics when I travel to the US - it's cheaper to pay the doctors to prescribe me medication there and fill it there than it is to fill it here. Plus, the clinics are willing to actually prescribe things like migraine medication and ADHD medication without simply telling me that I'll have to see a specialist, where I have to wait 3 months just to get an appointment.

Industrial welders can make six-figures easily. (Work on an oil rig.) Truck drivers and heavy machinery operators also.

And PS, so can nurses (at least in much of Canada), with just a bit of overtime.

Are you that white-collar that you have no idea that blue-collar people can actually earn quite well?

A large number of results that are likely to be null, I mean.

There's a trade-off between sensitivity and specificity. If you increase the threshold for "significance", you reduce the power to discover a significant effect when it truly does exist.

And a major part of the problem with scientific studies is that they are already underpowered. According to conventional wisdom, ideally, scientists should strive for a power of about 80% (i.e., an 80% chance of detecting an effect if it truly exists), but very few studies actually achieve power of this level. In many fields, the power is less than 50% and sometimes much less.

Underpowered studies result in two major problems:
1) Most obviously, an underpowered study results in a greater number of FALSE NEGATIVES. You fail to find a true effect. You will either publish your incorrect result of no effect. (And why should we consider published false positives to be any worse than false negatives?) Alternatively, perhaps you don't publish your study because you couldn't reach significance. This exacerbates the "file-drawer effect" and also results in wasted research dollars because the results aren't published.
2) Somewhat counterintuitively, underpowered studies are often also more likely to result in FALSE POSITIVES. This is because, when your power to detect a true effect is low, and if you test a large number of effects that are unlikely to be null, most of the hypotheses that you say are "significantly" non-null will actually be false positives. We would say that the "false discovery rate" tends to be very high when the power is low.

Reducing the level of significance will do little to address these problems, and in some cases may even exacerbate the problem.

The key is *to move away from the binary concept of "significance" altogether*. It's obviously artificial to have an arbitrary numerical cutoff for "matters" vs. "doesn't matter", and this is not what Ronald Fisher intended when he popularized the p-value or developed the concept of "significance".

What we should be doing is measuring and reporting effect sizes along with their credible intervals. While using priors that are based on our real state of knowledge. In other words, we should be doing Bayesian statistics.

Reimplementation also gives people the option to choose their own license. As developers, it's all too easy to succumb to "not invented here" syndrome, but there are sometimes very legitimate reasons to re-write things, even if they do exactly the same thing as existing solutions.

This "grenade" story is not at all true.

Her arm was blown off by a propane-tank IED built by the protesters. The remains of the tanks were confiscated after the explosion, and her clothing was taken for testing.
http://www.nytimes.com/2016/11...
http://bearingarms.com/bob-o/2...