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I've had a lot of positive comments on this theory, and some helpful challenges. The most common of which was: surely a single-day effect wouldn't be big enough to cause the 'twist' in the data that we're seeing in those age groups? So I set out to find out if it was (thread)

 This is one of those university / job interview 'order of magnitude' estimation problems. So feel free to disagree with any or all steps on my logic chain, and please explain why - it will help improve / refine (or falsify) the analysis.
So let's focus on the primary-school-age kids as that's where the effect is strongest. We have 3.5m 5-9 year-olds in England. I don't know how many were in school on 4th Jan - we know some regions (London / Kent etc.) didn't go back, and a lot of schools had INSET days etc
So I'm going to make a wild guess and say 40% were in school on that day. Better ideas (particularly if backed by data) very welcome. So that's about 1.4m children in school
Now ONS tells us that about 1.5% of that age group would test positive for coronavirus in early January. So that's about 20,000 kids with the virus heading into school.
(I've ignored the regional skew here - London would have higher prevalence and they weren't in school. But there are possibly some socio-demographic effects that cut the other way, so I think it's probably not that far out)
Now not all of those 20k are actually infectious - we know that ONS surveys using PCR can pick up people on the 'downslope' of viral load, past the point at which they can transmit. I have no good data here but am going to guess that 50% are potentially infectious - so 10k
So we have 10k infectious kids in school, surrounded by their classmates. How many will catch it? This is the hard bit, and I'm well outside my area of expertise. So I'm going to look at some other data which suggested secondary attack rates in households of c. 20%
i.e. if one of your housemates gets the virus, you have a 20% chance of catching it. If people are infectious for c. 10days, that's about 2% per day.
And I'm going to assume that a primary school classroom is about as good a place to pass on the virus as a household, with not much social distancing, lots of sharing stuff, sneezing and coughing, no masks etc.
So with 30 kids in the classroom, each with a 2% chance of getting it, we'd expect 0.6 of a child to catch it on that day, for each previously-infectious child (I've ignored the small possibility that there's more than one infectious child in a classroom)
and 0.6*10k= 6k new infections. But we know that children are a lot less susceptible to catching the virus than adults - probably around 25-30% from earlier studies I think. So those attack rates are too high. So let's scale down our new infections to maybe 1.5-2k.
And of course, not all infections turn up as cases in the https://t.co/KtOGeHLdHx dashboard data. So we need to scale that down a bit further. Let's say to maybe 500-1000 new cases we'd expect to see in the dashboard (spread over a few days).
The question is: is that enough to cause the 'twist' in the curve that we've seen? And the answer is: yes, it is.
we can work this out by going back to our log plot for the 5-9 year olds, building a linear trend off the first few days, and then measuring the number of cases under the 'bump'
It may help to know that the average daily case rate for this age group is only about 400 per day in England (in early/mid Jan), so you don't need a lot of cases to shift it. And if you add up the cases under the first week of the 'bump' then it's about 600
(it gets messy after the first week, because you start to get fast secondary cases mixed up with delayed primary cases, given the 7day averages we're using, so I think the first 7 days is a reasonable period to look at - views welcome)
so we needed about 600 cases to generate the 'bump' that creates the cubic twist in our log plot. and I've shown a logic as to how a single-day-mixing effect could plausibly create around 500-1000 new cases on the dashboard, spread over a few days
As I say, feel free to disagree with the numbers or logic. but our shared intuition (mine, too, before I started doing this analysis) that a single-day effect couldn't produce a noticeable pattern in the case data might just be wrong /end

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Or, why does YMM often respond with trivial feedback (e.g. formatting) & not substantive feedback?

Answers in this thread👇🏾

Have you set an entirely new password on a site & said to yourself: “surely, I’ll remember it becos this site is so important for me”. Have you then gone on to forget that password the very next week?

Me too.

The reason for YMM’s odd behavior & my password optimism is the same.

That reason is the Focusing Illusion, first described by Daniel Kahneman.

The Focusing Illusion:
Nothing in life is as important as you think it is while you are thinking about it.

When YMM asked you to create that doc as one of the follow up actions during the Product Review, YMM fell prey to the Focusing Illusion.

Trust me, YMM had no intention of making you do busywork.

YMM truly believed that it was very impt & urgent to create that doc & review it.

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1/

NDAs are traditionally “non-disclosure” - don’t talk about specific X. Often in church/ministry cases - they’re overly broad - don’t talk abt *anything* re: your employment

but also may include

“non-disparagement” - don’t say anything negative about us EVEN IF IT IS TRUE
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Numerous articles have described the use of NDAs in Christian environments and the conflict with principles described in the very faith supposedly being promoted. I’m linking them here then I’ll offer my 20-year lawyer and victim of silencing (gag order) take on the practice.
3/

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Survivorship Bias 101

History is written by the victors. But if we exclusively focus on these successes, we allow survivorship bias to distort our understanding of the world.

But what is "survivorship bias" and how does it work?

Here's Survivorship Bias 101!

👇👇👇


1/ First, a few definitions.

Survivorship bias is the logical error of concentrating on survivors (successes) and ignoring casualties (failures).

When we do so, we miss the true "base rates" of survival (the actual probability of success) and arrive at flawed conclusions.

2/ When we completely ignore failures, we lose our ability to correctly identify the differences between successes and failures.

Put simply, exclusively focusing on successes may actually inhibit our ability to identify (and replicate) the actions that led to such success.


3/ Cicero wrote on the topic over 2,000 years ago.

An atheist named Diagoras is shown portraits of people who prayed and were saved from death at sea as proof of God's existence.

Diagoras replies, "I see those who were saved, but where are those painted who were shipwrecked?"


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Survivorship bias can lead to a deeply-flawed understanding of cause-and-effect relationships.

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This response to my tweet is a common objection to targeted advertising.

@KevinCoates correct me if I'm wrong, but basic point seems to be that banning targeted ads will lower platform profits, but will mostly be beneficial for consumers.

Some counterpoints 👇


1) This assumes that consumers prefer contextual ads to targeted ones.

This does not seem self-evident to me


Research also finds that firms choose between ad. targeting vs. obtrusiveness 👇

If true, the right question is not whether consumers prefer contextual ads to targeted ones. But whether they prefer *more* contextual ads vs *fewer* targeted

2) True, many inframarginal platforms might simply shift to contextual ads.

But some might already be almost indifferent between direct & indirect monetization.

Hard to imagine that *none* of them will respond to reduced ad revenue with actual fees.

3) Policy debate seems to be moving from:

"Consumers are insufficiently informed to decide how they share their data."

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IMO the latter statement is incorrect.
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There is a highly instructive symposium on "Veil of Ignorance" #ProcessTracing in the new @APSAtweets QMMR publication https://t.co/nxqEQ9HaRz There is lots to say ab it. For now, I want to focus on whether (causal) qualitative research has a problem w/ confirmation bias that 1/

needs to be addressed.
In short, Veil of Ignorance process tracing aims to blindfold the data collectors and analysts to the hypotheses to reduce risk of confirmation bias.
Proposal receives a lot of criticism by all respondents, partly for valid reasons, partly not, IMO 2/

If there was no bigger issue with confirmation bias, there would not be much benefit in talking about blinding in qualitative research.
As Beach notes, the intro to the symposium does not present evidence that bias is a problem. This is correct, but there is also no evidence 3/

that confirmation bias is not a problem. It's an absence of evidence situation with regard to comprehensive, direct evidence, i.e. from verification studies of qualitative research (for studies that are built on an epistemology for which the idea of verification makes sense). 4/

However, I'd say there is circumstantial evidence that confirmation bias is a problem.
1) Evidence for confirmation bias in quantitative research: The developments around #OpenScience and transparency in quantitative research in different disciplines present diverse evidence 5/
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