Talk:Lucy Letby

Alleged Method

• Potassium Chloride: easily available, easy to administer, kills quickly, quickly dissipates and is hard to detect.

Resources

Caption text

Category	Daniela Poggiali	Lucia de Berk	Jane Bolding	Lucy Letby
“Incriminating” evidence	Took selfies laughing with dead body She had the ability to switch between wings of hospital Fiery temper, played pranks on colleagues Found patients irritating Allegations of thefts of jewellery	Example	Example	Googled parents That post-it note The increased insulin without corresponding C-peptide in 2 victims Had lots of teddy bears
Motive	“She must have just loved killing people”	Example	Example	Example
Actual evidence	Correlation between shifts and deaths	Example	Example	Correlation between shifts and deaths
Mitigants	Large ward many patients High proportion of very old and terminally ill patients Data collection was pretty ropey	Example	Example	Example
Alleged Method	Pottassium Choloride	Example	Example	Several
Resources	Richard Gill blog	Example	Example	New Yorker article We need to talk about lucy letby podcast

Peasant 2:

Peasant 1: We have found a witch may we burn her?
Peasant 2: Burn her!
Bedevere: How do you know she is a witch?
Peasant 1: She looks like one
Bedevere: Bring her forward.
Woman: I am not a witch
Bedevere:But you are dressed as one
Woman: They dressed me up like this
Peasant 1: We didn't!
Woman: And this isn't my nose it's a false one
Bedevere:Well?
Peasant 1: Well, we did do the nose
Bedevere: The nose?
Peasant 2: And the hat but she is a witch
Peasant 1: Burn her!
Bedevere: Did you dress her up like this?
Peasant 1: No.
Peasant 2: Yes.
Peasant 3: Yes.
Peasant 3: A bit. She has got a wart.
Bedevere: What makes you think she is a witch?
Peasant 1: Well, she turned me into a newt.
Bedevere: A newt?
Peasant 1: I got better.
Peasant 2: Burn her anyway!
Peasant 1: Burn her!
Bedevere: Quiet! There are ways of telling whether she is a witch.
Peasant 1: Are there? What are they? Tell us.
Bedevere: Tell me, what do you do with witches?
Peasant 1: Burn them!
Bedevere: What do you burn apart from witches?
Peasant 1: More witches!
Peasant 2: Wood.
Bedevere: So, why do witches burn?
Peasant 1: Because they're made of wood?
Bedevere: Good!
Bedevere: So, how do we tell whether she is made of wood?
Peasant 1: Build a bridge out of her!
Bedevere: Can you not also make bridges of stone?
Peasant 1: Oh, yeah.
Bedevere: Does wood sink in water?
Peasant 1: No. It floats! Throw her into the pond!
Bedevere: What also floats in water?
Peasant 1: Bread.
Peasant 2: Apples.
Peasant 3: Very small rocks.
Peasant 4: Cider.
Peasant 5: Cherries.
Peasant 6: Gravy.
Peasant 7: Mud.
Peasant 8: Churches.
Peasant 9: Lead.
Arthur: A duck!
Bedevere: Exactly. So, logically —
Peasant 1: If she weighs the same as a duck, she's made of wood.
Bedevere: And, therefore?

Peasant 1: A witch!
Peasant 1: A witch!

Holmes

JHB: what could have been the tell-tale signs that this woman was a danger? Was there anything that could have given us forewarning? Holmes: “Well, it may not give forewarning but generally speaking, [with] a serial killer of this stature really it’s almost obligatory to have psychopathic traits and they will often show themselves in various ways. ... she was very controlled. She was trying to give the image of a very responsible, caring nurse who would be there in a crisis to save babies and so on and so forth. So she is playing the role of someone who would not be suspected other than the correlation between her and the babies’ deaths.”

Sky: “It’s a situation where you have not got any really concrete evidence: one piece, like a CCTV camera footage or a witness, etc, all you’ve got is an accumulation of basically very low-level evidence — coincidences, etc — but when you actually accumulate a large number of these [using] something called a Bayesian analysis, it’s actually more statistically sound to have 100 little arrows pointing towards Lucy and none pointing away from her, and I think that’s how justice was actually reached.”

Well, to a point. Bayesian analysis starts with a “prior probability” — an initial estimate of the likelihood of the hypothesis that “Lucy Letby murdered multiple neonatal infants with no motive, no psychiatric history and no prior tendency” — which is extremely low — and updates it on the cumulative effect of the “little arrows” to revise the likelihood of it being true. An accumulation of even weak “little arrows” can increase the “posterior” probability of the hypothesis, but if all the arrows are also consistent with another explanation, their contribution to updating a Bayesian analysis may be modest. There is significant potential for false positives when dealing with rare events and weak evidence. (Compare this with David Bain’s case, where the prior probability that he was the culprit was already high (the event was certainly murder, and he was one of only two plausible suspects), and the circumstantial evidence against him was strong, and Lindy Chamberlain, where the prior probability was very low, and the circumstantial evidence weak).

Caption text

Case	There was a Murder		Suspect responsible
	Prior	Posterior	Prior	Posterior
OJ Simpson	Certain	Certain	Highly likely	Unchanged
David Bain	Certain	Certain	Fairly likely	Greatly increased
Lindy Chamberlain	Very Unlikely	Mildly increased	Extremely Unlikely	Mildly increased
Lucy Letby	Very Unlikely	Mildly increased	Extremely Unlikely	Mildly increased

The question, then, is how many of the “little arrows” are not consistent with another explanation. Here there are two categories of alternative explanation: that someone else was responsible, or that no-one was criminally responsible: the event would have happened anyway.

For the same reason, the prior probability of there being a different murderer on the ward is extremely unlikely.