Meeting: the statistics of drug testing in sport

The next section meeting will be held at the Royal Statistical Society, Errol Street on 18 April 2019 from 2pm-5pm.

There have been a number of high-profile sports doping cases in recent years. Famous examples include the Russian Olympic scandal, Lance Armstrong, Justin Gatlin and many more. But how can cheating be distinguished from a good performance? How can abnormal measurements in drug tests be differentiated from measurement error? And how sure must the evidence be to punish athletes for failed drug tests? This event will bring together four experts in the field to discuss their experience in the statistics of drug testing. Following the presentations we will have an open discussion on the questions raised.


  • Professor Andrea Petroczi, Kingston University
  • Dr Reid Aikin, World Anti-Doping Agency
  • Professor Sheila Bird, Medical Research Council Biostatistics Unit, Cambridge
  • Professor Don Berry, The University of Texas MD Anderson Cancer Center, Houston

Event Fees:
Fellows: Free
Non-Fellows: £25

Registration is required. The link to register is here:




House of lords inquiry into forensic science

The science and technology committee of the House of Lords recently conducted an enquiry into the use of forensic science in the criminal justice system. The inquiry looked at the research landscape in forensic science, standards and regulation, and digital forensics.

The Section submitted written evidence to the enquiry. This evidence has been published here:


AGM and meeting on Policing and Criminology

The next meeting of the section will be held on 10 December 2018 from 14.00-17.00 at the Royal Statistical Society, 12 Errol Street, London EC1Y 8LX. The AGM of the section will be from 13.40-14.00, immediately preceding the meeting.

The meeting will be on policing and criminology. We will have four speakers:

  • Professor Jim Smith (University of Warwick/ Alan Turing Institute)

Graphical Models to Help Investigate Violent Criminals

One serious challenge in providing support for policing various kinds of systematic violent crime is that cases can be very dynamic and idiosyncratic. In this talk I will outline the progress we are making in designing new graphical interfaces that help to separate the enduring structure of these processes from their more ephemeral features. Our Bayesian models have derived from earlier studies concerning the synthesis forensic activity level evidence but are now applied to resourcing models to support the prevention of crime. This reports on ongoing work undertaken by a team of researchers at the Alan Turing Institute.

  • Dr Anjali Mazumder (Carnegie Mellon University/ Alan Turing Institute)

Algorithmic Tools in Justice – bias and fairness, a causal lens

There has been an increasing use of algorithmic tools to support decision-making across public sectors, including financial, human, health care, policing and criminal justice services. The use of such algorithms is not new. However, with growing recognition of the bias and potential for unfairness that such tools may possess and perpetuate, researchers have begun to develop methods to achieve algorithmic fairness. In this talk, we discuss the use of algorithmic tools in justice, their potential for and inherent bias and implications on fairness in such high stakes decision-making, and approaches to achieve algorithmic fairness. We will take a particular causal lens to explore the fairness of algorithmic tools in which forensic science plays a central role in both the investigative and evaluative stage of a criminal case. This latter work reports on ongoing work undertaken by researchers at the Alan Turing Institute and Carnegie Mellon University.

  • Professor David Tuckett (Psychoanalysis Unit, UCL)

Making Decisions under Radical Uncertainty

How can academic study help business-leaders, policy-makers, regulators or those in in a courtroom, make better decisions? For a long time now, the answer has been by using tools such as game theory, expected utility theory, subjective utility theory to provide them with optimal choices – and often with major success. However, are these tools always – or even usually – appropriate? What role do specifically human qualities of imagination, feeling and intuition have to play? What is the role of analysing “data” properly and is the conclusion to draw from academic research that humans are poor decision-makers, influenced by bias and emotion, so that we would be better relying on AI as often as possible?

This talk will review these questions by introducing the work of the UKRI funded CRUISSE[1] network and introducing Conviction Narrative Theory, as a model for human decision-making when there is deep uncertainty.

[1] Confronting Radical Uncertainty in Science, Society and the Environment.

  • Dr Toby Davies (Jill Dando Institute of Security and Crime Science, UCL)

Understanding and predicting urban patterns of crime

One of the most crucial steps in preventing crime is understanding where and when it happens: as well as providing a basis for the deployment of police resources, such insight also provides a rationale for the application of place-based interventions. Traditionally, gaining such insight has been a particular challenge, given the complexity of behaviours involved, and its utility has been primarily descriptive. In recent years, however, improved data availability, coupled with the application of analytical techniques from other fields, has revealed a number of statistical regularities in crime data; most notably, its heterogeneous distribution in space and time and the prevalence of space-time clustering. In turn, the presence of these regularities has raised the possibility that they can be leveraged in order to predict the locations of future crimes by applying algorithms to past crime data. In this talk, I will briefly review this background, before discussing recent research which examines the role that urban structure – in particular the street network – plays in shaping these patterns. I will discuss the implications of this for crime prediction, and show how the adaptation of algorithms to account for this structure leads to improved predictive performance. In conclusion, I will describe a real-world implementation of predictive policing and identify opportunities for further exploitation of data in the field.

Attendance at the meeting is free of charge but registration is required. The registration link can be found here:


Colloquium – the UK civil law approach to epidemiology/ statistical evidence

The next meeting of the section will be on 11 June at Fountain Court Chambers. We will be discussing the role of epidemiological and statistical evidence in civil law. A range of speakers will present their view and there will be time for discussion. Sign-up for this meeting is required as the number of attendees is limited. The registration page can be found here:


Date:     11 June 2018 – 2-5pm

Venue:  Fountain Court Chambers, London EC4Y 9DH

Chair:  Dr Claire McIvor


  • Practitioner’s view by Leigh-Ann Mulcahy QC
  • Judge’s view by Mr Justice Stuart-Smith
  • Legal academic’s view by Professor Jane Stapleton
  • Statistician’s view by Professor Jane Hutton
  • Epidemiologist’s view by Professor Alan Silman
  • Open discussion


To consider the approach in case-law regarding the validity and application of epidemiological and statistical evidence in UK civil law and in particular:

  • Whether, and in what circumstances, statistical evidence can be used on its own to prove causation;
  • the validity and application of the “doubles the risk” test to (a) proof of defect and (b) proof of factual causation;
  • whether there is confusion about how the civil (balance of probabilities) standard of proof operates and its relationship with factual causation;
  • whether the right experts are being used in court in relation to these issues;
  • the differences between epidemiological and statistical evidence;
  • the relevance of the Bradford-Hill criteria;
  • whether there are any gaps evident in understanding/approach between the law and statistics/epidemiology and, if so, how these might best be bridged?

 Relevant cases:

XYZ v Schering Health Care [2002] EWHC 1420 (QB)

Sienkiewicz v Grief (UK) Ltd [2011] 2 AC 229, Supreme Court

Heneghan v Manchester Dry Docks Ltd [2016] 1 WLR 2036

Metal on Metal Hip Litigation judgment (forthcoming)

Relevant materials:

ICCA/RSS guide “Statistics and probability for advocates: Understanding the use of statistical evidence in courts and tribunals” at pp.67-70:

Dr Claire McIvor: “Debunking some judicial myths about epidemiology and its relevance to UK tort law” Med.L.Rev 2013, 21(4), 553-587

Professor Jane Stapleton: “Factual causation, mesothelioma and statistical validity” LQR 2012, 128 (Apr), 221-231

Dr Gemma Turton: Evidential Uncertainty in Causation in Negligence (Hart, 2016)

Professor Philip Dawid: “The Role of Scientific and Statistical Evidence in Assessing Causality” in Professor R Goldberg (ed) Perspectives on Causation (Hart, 2011)

AGM and cyber crime meeting

The next meeting of the section will take place on Tuesday 17 October 2017 from 14.00 to 16.30 at the Royal Statistical Society, 12 Errol Street, London EC1Y 8LX.

The annual general meeting will be held from 13.40 to 14.00. Following the AGM there will be a joint meeting with the Data Science Section on cyber crime featuring talks by Professor Niall Adams (Imperial College London) and GCHQ. The schedule is below:

14:00 – 14:30 Coffee and Tea

14:30 – 15:30 GCHQ speaker. Title: Data science for security at GCHQ. GCHQ

15:30 – 16:30 Prof. Niall Adams. Title: On Constructing Cyber-Analytics. Department of Mathematics, Imperial College, London

This event is free to attend but registration is required on the following link:



GCHQ speaker

The Government Communications Headquarters (GCHQ)

Title: Data science for security at GCHQ

This talk will give a brief survey of data science for cybersecurity at GCHQ, and some thoughts on longer-term challenges for the statistics community.


Prof. Niall Adams

Department of Mathematics, Imperial College, London

Title: On Constructing Cyber-Analytics

Enterprise network defense is providing great opportunities for the development and deployment of statistical and machine learning methods. Such methods are intended to complement existing defenses, such as firewalls, virus scanners, and intrusion detection systems – which are predominantly signature-based. The role of data analysis methods is to provide enhanced situation awareness, by providing monitoring and alerting mechanisms to detect departures from “normal” behavior. In developing analytics in this context, a variety of challenging problems need to be addressed, including the volume and velocity of the data, high levels of heterogeneity, temporal variation, and more.  We review aspects of the problem and characteristics of the various data sources.  At present, the vision of jointly modelling various data sources at different levels of network abstraction, appears out of reach due to data volume and timeliness concerns. Instead, we describe a set of novel, and often simple, analytics that operate within different levels of the abstract hierarchy.


Slides from joint meeting with RSS local group

Slides from the talks at the joint meeting with the Glasgow RSS local group on 27 February can be found on the local group webpage:

Slides can be downloaded by clicking the link next to the speaker’s name.

Thanks to the speakers Professor Jane Hutton and Dr Tereza Neocleous for an excellent meeting! For a summary, see the following link: