Yes, if the potential effect size is large, you can get away with imprecise answers to some questions. But if there are many questions, at some point your "imprecision budget" will be spent. For example, will you be able to detect if your dosing leads to later hospitalization instead of no hospitalization? Or it weakens immunity instead of strengthening it?
Let's say X% get hospitalized within 2 weeks. What's the highest value of X that would say variolation is a good idea? Keep in mind that:
The demographics of your sample aren't the same as the general population, hopefully you didn't include many 60+ folks.
You don't know how many botched the protocol. Could botch in any direction (dose too high, too low, or no dose at all).
You don't know the hospitalization rate after contacting corona in normal ways, which can also be low dose. Many people don't get tested now and the epidemic is spreading.
Etc.
Spain has stabilized at 7K new cases/day, Italy at 5K new cases/day. At this rate it will take many months to reach a significant percentage of the population. The same will probably happen in the US. Most people won't get infected, so trying amateur vaccination is more dangerous than doing nothing.
How will you send doses to volunteers? If I were a delivery company, I would refuse to deliver this and would call the cops.
How will you measure the results? People have trouble measuring the death rate from corona, sometimes they can't even agree on the order of magnitude. It's really low and depends on demographic factors, environment, treatment and other things that aren't well understood. If you want to measure a change in that rate by looking at 10k remote volunteers in reasonable time, I'd like to see your methodology and error bounds.
Counterpoint: most people who will read your post are already better than average at vetting-memes-before-spreading. If you succeed at making these folks even more cautious, everyone else in the world will still keep spreading unvetted memes, so worse memes will win.
Wait, so your graph shows the number of people having their 2-day "infectious period" at any given time, which could be much lower than the number of people infected at a given time? That doesn't seem to be explained on the page.
Anyway, I think the really important number is how many people are having their "required hospitalization period" at any given time (which is longer than 2 days). Maybe you could show that too, since you're already showing the "care capacity" line?
It still looks weird to me. For example, in Switzerland with no mitigation it estimates 1% of people infected now and 3% at the peak on Apr 14, which is 2.5 weeks from now. Since each infection lasts a couple weeks or more, and there have been few deaths and recoveries so far, that means <5% of the population will have been infected by that point. And then it says active infections will start falling. Why?
Does anyone know why the dashboard says infections will peak at 3% if no mitigation is done?
I think there are two issues here: 1) what are the right beliefs to have about life 2) what's the right emotional attitude to life. You paint a picture of truth as a harsh destroyer of illusions, but why not describe it as a source of wonder / beauty / power / progress instead?
In terms of conversation style, I'd define a "rationalist" as someone who's against non-factual objections to factual claims: "you're not an expert", "you're motivated to say this", "your claim has bad consequences" and so on. An intermediate stage would be "grudging rationalist": someone who can refrain from using such objections if asked, but still listens to them, and relapses to using them when among non-rationalists.
The phrasing "being right isn't enough" seems a bit off, because the second machine is right more often than the first. So maybe the post is more about calibration vs confidence. But there's no tension between them, because they can be combined into log score - a single dimension that captures the best parts of both.