Saturday, April 10, 2010

Η Στατιστική και η Δικαιοσύνη

Ανανέωση: (15/4/10). Nurse Lucia de Berk not guilty of murdering seven patients
Nurse Lucia de Berk has been formally found not guilty of murdering seven patients and attempting to murder three more, ending one of the biggest miscarriages of justice in Dutch legal history.
De Berk, who always maintained her innocence, was jailed for life in 2004.
The case against her was largely based on statistical evidence and claims that a baby had been poisoned.
That supposed murder, later disputed by toxicologists, led prosecutors to state that other patients had also been killed by her.

Είναι αναρίθμητες οι περιπτώσεις που η Στατιστική έχει παίξει καθοριστικό ρόλο (θετικό ή αρνητικό) στην απονομή της δικαιοσύνης.

Το τελευταίο παράδειγμα είναι αυτό της Ολλανδής νοσοκόμας Lucia de Berk, που καταδικάστηκε σε ισόβια πριν έξι χρόνια ως υπεύθυνη για τον θάνατο επτά ασθενών στα νοσοκομεία που δούλευε.

Στην καταδίκη της, συνέβαλε ένας καθηγητής νομικής που (ισχυριζόταν ότι) ήξερε Στατιστική. Στην αναμενόμενη αθώωσή της καθοριστικό ρόλο έχει παίξει ένας πραγματικός στατιστικός, ο καθηγητής Richard Gill, που εκμηδένισε όλα τα επιχειρήματα που είχαν προβληθεί. Του αξίζουν συγχαρητήρια. Οχι μόνο γιατί έθεσε την πραγματική επιστήμη στην υπηρεσία της κοινωνίας, αλλά και γιατί πήρε πάνω του την διόρθωση μια τρομερής αδικίας.

Το πλήρες άρθρο στον σημερινό Guardian.

Lucia de Berk is a Dutch nurse who has spent six years of a life sentence in jail for murdering seven people in a killing spree that never happened. She will hear about her appeal on Wednesday, and there is now little doubt that she will be cleared. The statistical errors in the evidence against her were so crass that they can be explained in one newspaper column. So will the people who jailed her apologise?

The case against Lucia was built on a suspicious pattern: there were nine incidents on a ward where she worked and Lucia was present during all of them. This could be suspicious but it could be a random cluster, best illustrated by the "Texas sharpshooter" phenomenon: imagine I am firing a thousand machinegun bullets into the side of a barn. I remove my blindfold, find three bullets very close together and paint a target around them. Then I announce that I am an Olympic standard rifleman.

This is plainly foolish. All across the world, nurses are working on wards where patients die, and it is inevitable that on one ward, in one hospital, in one town, in one country, somewhere in the world, you will find one nurse who seems to be on a lot when patients die. It's very unlikely that one particular prespecified person will win the lottery but inevitable someone will win: we don't suspect the winner of rigging the balls.

And did the idea that there was a killer on the loose make any sense, statistically, for the hospital as a whole? There were six deaths over three years on one ward where Lucia supposedly did her murdering. In the three preceding years, before she arrived, there were seven deaths. So the death rate on this ward went down at the precise moment that a serial killer moved in.

Even more bizarre was the staggering foolishness by some statistical experts used in the court. One, Henk Elffers, a professor of law, combined individual statistical tests by taking p-values – a mathematical expression of statistical significance – and multiplying them together. This bit is for the nerds: you do not just multiply p-values together, you weave them with a clever tool, like maybe 'Fisher's method for combination of independent p-values'. If you multiply p-values together, then chance incidents will rapidly appear to be vanishingly unlikely. Let's say you worked in 20 hospitals, each with a pattern of incidents that is purely random noise: let's say p=0.5. If you multiply those harmless p-values, of entirely chance findings, you end up with a final p-value of p < 0.000001, falsely implying that the outcome is extremely highly statistically significant. By this reasoning, if you change hospitals a lot, you automatically become a suspect.

One statistician — Richard Gill — has held the Dutch courts' feet to the fire, writing endless papers on these laughable statistical flaws ( Alongside the illusory patterns he has identified, there was one firm piece of forensic evidence. Some traces of the drug digoxin were found in one baby who died. The baby had previously been prescribed digoxin, months previously. Three court toxicologists now say the digoxin was not the cause of death.

Even the Dutch state prosecution now accepts Lucia should be acquitted and there was no evidence of any unnatural deaths, though her convictions for stealing two books from the hospital library – a charge she denies – will be upheld. Now living with her partner while awaiting judgment, Lucia is penniless, denied benefits, and paralysed down one side following a stroke she had in 2006 in the week she was told her conviction would be upheld.

Watch what the Dutch legal system does next because it owes her a great deal.


  1. Anonymous5:21 am

    Ενδιαφέρον. Αλλά ενόψη προσφυγής στο ΔΝΤ, μήπως πρέπει να θίξετε και μερικά θέματα πιό σχετικά;

    Γράφει το ΕΘΝΟΣ σήμερα για μείωση της επιχορήγησης των μεταπτυχιακών ανά τμήμα.

    Τι άλλο σκοπεύετε να κάνετε για περικοπή δαπανών;

    Θα υπάρξουν δίδακτρα; Θα μειωθούν οι θέσεις των φοιτητών; Θα κλείσουν ή συγχωνευτούν τμήματα;


  2. Κύριε Πανάρετε,
    μετά την περικοπή του προϋπολογισμού των Πανεπ./ΤΕΙ, ίσως είναι σκόπιμο να αξιοποιηθούν οι ομότιμοι καθηγητές των ΤΕΙ, αμισθί και εφόσον ενδιαφέρονται. Θα μπορούσαν να αναλάβουν τη διδασκαλία ενός μαθήματος και παράλληλα να αυξηθεί το ωράριο των τακτικών εκπαιδευτικών, π.χ. κατά τα επόμενα 3 χρόνια, για 2 ώρας την εβδομάδα.
    Θα καλυφθούν έτσι πολλές ανάγκες και θα μειωθούν τα έξοδα πρόσληψης εκτάκτων εκπαιδευτικών.
    Επειδή οι ομότιμοι επιτρέπεται τώρα να διδάσκουν μόνο σε μεταπτυχιακά προγράμματα (με αμοιβή), θα έπρεπε να διορθωθεί αυτή η λεπτομέρεια νομοθετικά, ώστε να αξιοποιηθούν και σε προπτυχιακά μαθήματα - εφόσον το επιθυμούν και χωρίς αμοιβή, επαναλαμβάνω.