1/ Thanks to @BBCr4today for having me on to discuss data collection on sex and why it matters. This follows the extraordinary claim by Scotland's Chief Statistician, Roger Halliday, that sex should typically not be asked unless there is a medical reason.

2/ In fact, those of us who use quantitative data overwhelmingly believe that sex is important. It matters across a wide range of domains: education, wages, crime, political attitudes, religion - you name it, sex is almost always a big predictor!
3/ Sex and gender identity are two different things, and gender identity is not a clarly defined concept. Ciaran McFadden Young (who is not a quantitative social scientist as far as I can see) claimed that sex doesn't matter, effectively it is always trumped by gender identity.
4/ This is a remarkable claim, but it is a testable claim, if we collect data on both sex and gender identity. If we can't collect the data, then we will never be able to test this hypothesis. And perhaps that's the point.
5/ Ciaran also claimed that is has been proven that post-transition MtF transwomen earn the same as https://t.co/6luJGGHikU was frustrating not to have a chance to challenge this claim, which I find implausible. I'd be very interested to see the research referred to @dr_ciaran
6/ Finally, a point I didn't get a chance to make: public bodies have changed their data collection practices in response to lobbying by organisations such as Stonewall, who have also lobbied for the removal of sex as a protected characteristic under UK law.
7/ They have not actively consulted the community of quantitative social scientists who use data to understand social problems and social trends. In fact they have disregarded our views. This risks bringing public statistics into disrepute.
Listen from around 1.50 https://t.co/DnsJFC5ejK
A tiny correction: I was introduced as being from the @ucl Sociology department. In fact I am at the UCL Social Research Institute @UCLSocRes . (UCL doesn't have a Sociology department in fact).
If you want to read more on sex, gender identity and data collection: https://t.co/oleT1zoa0Z
And if you'd prefer to watch/listen: https://t.co/58U0L1Yahh

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global health policy in 2020 has centered around NPI's (non-pharmaceutical interventions) like distancing, masks, school closures

these have been sold as a way to stop infection as though this were science.

this was never true and that fact was known and knowable.

let's look.


above is the plot of social restriction and NPI vs total death per million. there is 0 R2. this means that the variables play no role in explaining one another.

we can see this same relationship between NPI and all cause deaths.

this is devastating to the case for NPI.


clearly, correlation is not proof of causality, but a total lack of correlation IS proof that there was no material causality.

barring massive and implausible coincidence, it's essentially impossible to cause something and not correlate to it, especially 51 times.

this would seem to pose some very serious questions for those claiming that lockdowns work, those basing policy upon them, and those claiming this is the side of science.

there is no science here nor any data. this is the febrile imaginings of discredited modelers.

this has been clear and obvious from all over the world since the beginning and had been proven so clearly by may that it's hard to imagine anyone who is actually conversant with the data still believing in these responses.

everyone got the same R

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