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Experts warn Letby inquiry of misreading stats to explain 'suspicious' deaths

thejusticegap.com

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Re: Experts warn Letby inquiry of misreading stats to explain 'suspicious' deaths

#81
post #22
post #2

Anybody who closely followed this case who could identify for the readers here any direct evidence of these crimes, or should we read this article to imply Lucy herself might be innocent?

There were various postmortem findings described as anomalous. Child F had a bloodwork which indicated that someone gave them a large amount of insulin. Child D’s post mortem X-ray is said to show air injected into their blood vessels. Child L also had insulin in their blood. I’m not an expert so I can’t say for certain how strong these evidence are. Also my understanding that while these are evidence that a crime ha…

>> Child F had a bloodwork which indicated that someone gave them a large amount of insulin.

>> Child L also had insulin in their blood.

Those were not post-mortems because these two children didn't die. Lucy Letby was accused of attempting to murder them, by injecting them with insulin.

See here, towards the end of the article, a summary of accusations and convictions:

https://www.nursingtimes.net/news/children/breaking-lucy-let...

Re: Experts warn Letby inquiry of misreading stats to explain 'suspicious' deaths

#82
post #11

While the statisticians are right to be cautious about clusters of deaths => serial killer, we also have to remember that a 9 month trial presented lots of evidence to a jury, they deliberated for over a week, and returned seven guilty verdicts. Unless you were also sitting at that trial or have read every line of court transcript, it would be very unwise to start claiming there's a miscarriage of justice.

Those arguments included that the suspicious deaths all lined up perfectly with Lucy Letby's shifts, that she was often caught interacting with those babies when she had been instructed not to, and that when she was taken off the ward the deaths instantly stopped. That is a level of causality checking far stronger than most Bayesian analyses.

[deleted]

Re: Experts warn Letby inquiry of misreading stats to explain 'suspicious' deaths

#83

Earlier quoted context omitted.

I suppose, but it wouldn't be the first time a judge or jury was swayed by dubious statistics. At least that is exactly what happened to Lucia de B [1] (which they mention in the article), which at first glance looks eerily similar. [1]: https://en.m.wikipedia.org/wiki/Lucia_de_Berk

Please; write 'Lucia de Berk', not 'Lucia de B'. She was exonerated in full, and has since indicated quite clearly that she wishes to be known under her full name, not the initialized suspect name.

Oh, seems I got that exactly the wrong way around. I was a bit on the fence about it, and figured not using her real name was the safest option.

However if Lucia de Berk herself wishes people to use her full name then that is a very noble decision. The edit window has passed unfortunately, but I will use her full name in future.

Re: Experts warn Letby inquiry of misreading stats to explain 'suspicious' deaths

#84
post #22

Earlier quoted context omitted.

There were various postmortem findings described as anomalous. Child F had a bloodwork which indicated that someone gave them a large amount of insulin. Child D’s post mortem X-ray is said to show air injected into their blood vessels. Child L also had insulin in their blood. I’m not an expert so I can’t say for certain how strong these evidence are. Also my understanding that while these are evidence that a crime ha…

>> Child F had a bloodwork which indicated that someone gave them a large amount of insulin. >> Child L also had insulin in their blood. Those were not post-mortems because these two children didn't die. Lucy Letby was accused of attempting to murder them, by injecting them with insulin. See here, towards the end of the article, a summary of accusations and convictions: https://www.nursingtimes.net/news/children/brea…

You are correct. Thank you for the correction.

Re: Experts warn Letby inquiry of misreading stats to explain 'suspicious' deaths

#85
post #11

While the statisticians are right to be cautious about clusters of deaths => serial killer, we also have to remember that a 9 month trial presented lots of evidence to a jury, they deliberated for over a week, and returned seven guilty verdicts. Unless you were also sitting at that trial or have read every line of court transcript, it would be very unwise to start claiming there's a miscarriage of justice.

Those arguments included that the suspicious deaths all lined up perfectly with Lucy Letby's shifts, that she was often caught interacting with those babies when she had been instructed not to, and that when she was taken off the ward the deaths instantly stopped. That is a level of causality checking far stronger than most Bayesian analyses.

I made another post yesterday on the argument that "that the suspicious deaths all lined up perfectly with Lucy Letby's shifts":

https://news.ycombinator.com/item?id=37814339

Those arguments, as presented to the jury, relied entirely on a spreadsheet of the nurses' duty roster. There's an image here (on the Daily Mail website):

https://i.dailymail.co.uk/1s/2023/08/18/22/74487747-12286051...

Looking at this spreadsheet the one thing that immediately jumps out to me is that this spreadsheet is not the entire data.

The spreadsheet shows only the days where Lucy Letby was on shift and there was a suspicious incident (i.e. when a baby died or collapsed).

It is very difficult to believe that there were no shifts were incidents occurred and Lucy Letby was not on shift, or times Lucy Letby was on shift but no incident occurred.

Obviously, without that data that is not included in the spreadsheet it is impossible to draw any conclusions about Lucy Letby's correlation with the incidents she was convicted of causing. That's pretty much what the RSS letter points out, that:

it is far from straightforward to draw conclusions from suspicious clusters of deaths in a hospital setting – it is a statistical challenge to distinguish event clusters that arise from criminal acts from those that arise coincidentally from other factors, even if the data in question was collected with rigour.

And the data in that spreadsheet was certainly not presented with any rigour. Rather, this spreadsheet is a textbook example of cherry-picking. It only shows the data that justifies the prosecutor's claim.

I have no way to know whether this was done consciously, in order to mislead, or it was merely the result of poor understand of statistics by police officers and prosecutors. I'm inclined to believe the latter was the case.

In any case, _if_ the jurors were convinced by that spreadsheet of Lucy Letby's guilt, to whatever extent (weighing it in view of other evidence), then they did so without having enough information.

Re: Experts warn Letby inquiry of misreading stats to explain 'suspicious' deaths

#86

Earlier quoted context omitted.

"Apart from the general ridiculousness of insinuating that of all records someone might adulterate or invent, he would pick a digital copy of a discharge letter sent to an third party whose other baby had died, [..]" That's what you say. I neither said nor insinuated any of that. The case is supported by two main pillars. One is the idea that the stats support suspicions clusters of deaths. I expected that to be chal…

Why was Child F in the hospital? Is it posible to be a false positive? From https://www.google.com/search?q=c-peptide+false+positive https://labs.selfdecode.com/blog/c-peptide/ > However, while healthy kidneys are efficient at breaking down C-peptide, in people with impaired kidney function and kidney disease, blood C-peptide levels will falsely increase and urine levels will falsely decrease.

Child F, who survived without kidney problems, was in the hospital because he was born prematurely, along with a twin who suddenly died from a different cause the previous night, with nurse Letby seen standing over him earlier in the evening as he screamed and spat out blood.

I'm going to go out on a limb and say that the medical professionals who determined the evidence was conclusive had sufficient expertise not be relying on Google searches to interpret blood peptide readings..

Re: Experts warn Letby inquiry of misreading stats to explain 'suspicious' deaths

#87
post #11

While the statisticians are right to be cautious about clusters of deaths => serial killer, we also have to remember that a 9 month trial presented lots of evidence to a jury, they deliberated for over a week, and returned seven guilty verdicts. Unless you were also sitting at that trial or have read every line of court transcript, it would be very unwise to start claiming there's a miscarriage of justice.

Thanks for the reminder! I no longer will hold the belief that OJ was guilty!

Re: Experts warn Letby inquiry of misreading stats to explain 'suspicious' deaths

#88
post #55
post #33

Earlier quoted context omitted.

To a large extent they didn't have to involve statistics. There was plenty of better evidence in the Letby case. However, the public perception around the time of the trial was substantially different. Especially early on, the media focused strongly on the fact that it "could not possibly be a coincidence". Reading the trial coverage I got a very strong feeling of déjà vu, seeing the coverage pretty much mirroring th…

Better evidence is irrelevant to her jury trial if false evidence was included in the form of misrepresented stats. Because that's how trial by jury works. She would get a retrial, at minimum, if a judiciary body were to be convinced that the prosecution introduced false evidence.

From what I understand, they did not even attempt to introduce misrepresented stats as evidence in the Latby case - it simply wasn't needed. All the other evidence was already enough to get her convicted, so having better evidence led to the statistics becoming irrelevant.

Re: Experts warn Letby inquiry of misreading stats to explain 'suspicious' deaths

#89

I like to remember that the odds of winning the lottery are easily in the 1 in 50 million, yet people still seem to win it. It’s statistically improbably to get 7 sons, but I’m certain that throughout history that’s happened at least once.

At my primary school there were twin boys, with 3 older brothers, 1 younger and no other siblings so they were damn close!

Re: Experts warn Letby inquiry of misreading stats to explain 'suspicious' deaths

#90

Earlier quoted context omitted.

Those arguments included that the suspicious deaths all lined up perfectly with Lucy Letby's shifts, that she was often caught interacting with those babies when she had been instructed not to, and that when she was taken off the ward the deaths instantly stopped. That is a level of causality checking far stronger than most Bayesian analyses.

I made another post yesterday on the argument that "that the suspicious deaths all lined up perfectly with Lucy Letby's shifts": https://news.ycombinator.com/item?id=37814339 Those arguments, as presented to the jury, relied entirely on a spreadsheet of the nurses' duty roster. There's an image here (on the Daily Mail website): https://i.dailymail.co.uk/1s/2023/08/18/22/74487747-12286051... Looking at this spreadshee…

> It is very difficult to believe that there were no shifts were incidents occurred and Lucy Letby was not on shift, or times Lucy Letby was on shift but no incident occurred.

I agree. It's very strange that there was not at least one case when she was not there. Death are very clear, but it's easy to use different criteria to classify an weird case as suspicious.

I'd expect more cluster of nurses. There are a few that work (almost?) in all the cases during the day, but the nights are more random. Some nurses appear only one. Which one had a full time job there and which where hired only a few times?

There is a strange diagonal that starts at Child D - Nurse H. How did they sort the nurses? Alphabetically? By some internal HR number? [It may be an artifact, because people is too good detecting patterns like diagonals. I was generating a similar random grid a few years ago, and I saw too many patterns that I suspected the random generator was wrong. But it was just me overfitting the data.]

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