Data selection and confounding in the court case of Lucia de Berk

TL;DR

This paper analyzes the Lucia de Berk case, highlighting how data selection and confounding in statistical evidence led to a wrongful conviction, emphasizing the importance of proper data interpretation in legal contexts.

Contribution

It introduces the concept of nominal correlation to illustrate issues of data selection and confounding in forensic statistics, advocating for careful statistical analysis in court cases.

Findings

Data selection and confounding influenced the conviction

Nominal correlation highlights issues in statistical evidence

Potential for mistrial due to flawed data interpretation

Abstract

The nurse Lucia de Berk was convicted by the Dutch courts as a serial killer with 7 murders and 3 attempts at murder in three hospitals where she worked. The nurse however always professed her innocence and indeed was never observed in such an act of murder. The courts based their decision on circumstantial evidence and upon the use of statistics. In the appeal court, the use of statistical calculations was repealed but the use of "data" and "statistical insights" were not excluded. The trial hinged importantly on the role of statistics and data gathering. It appears that data selection and confounding feature strongly in this case. The notion of "nominal correlation" can be used to highlight those two features. This suggests a mistrial with the conviction of an innocent person.