Imperial College London

Hilary Watt CStat FHEA MSc MA(Oxon) BA

Faculty of MedicineSchool of Public Health

Teaching Fellow



+44 (0)20 7594 7451h.watt Website




322Reynolds BuildingCharing Cross Campus





Hilary leads Statistics Masterclasses, that allow taught post-graduate, BSc and PhD students and staff to all benefit from her innovative published teaching methods. She teaches conceptual understanding of p-values and confidence intervals (CIs). She uses definitions and images that intentionally correct widespread misconceptions, whilst simultaneously clarifying core concepts. With co-investigators, she is evaluating the variety of CI and p-value definitions. She gives statistics teaching talks at the Royal Statistical Society annual international conferences (and at other RSS events). She has given talks/ led discussions at the annual UK Burwall's medical statistics teaching conference. 

Hilary teaches preparing data for analysis, using both R and software, with a remit to oversee this aspects of post-graduate dissertations, as well as supporting staff and PhD students. These resources have received great acclaim for their practical focus and for the considerable time they can save. 

Hilary is statistician co-applicant for randomised controlled trial, evaluating drug treatment to improve attention (extended-release guanfacine) in patients with Alzheimer's disease, as an add-on therapy in patients already receiving memory-enhancing drugs (NIHR funded, PI Dr Paresh Malhotra). Hilary is co-supervising/ providing statistical input for three students who have been awarded NIHR fellowships and one further staff member who is undertaking her PhD. Hilary is a sought-after member of the NIHR Research Design Service, whose clients are highly appreciative of her expertise. 

Eminent statisticians have been expressing concern over standards of statistical interpretation in applied research over many years. This agenda has been actively embraced by the American Statistical Association over the past several years, with many resulting publications including in Nature. By tying her teaching methods in with this agenda, she earned a publication in the high-profile International Journal of Epidemiology in 2020. Key ideas are encompassed into her talk (view by clicking here).

Hilary performs a stand-up comedy act, as a homage to Florence Nightingale, including Florence's use of data to promote public health. She uses comedy to bring curious scientific facts to a wider audience, promoted via her YouTube channel. Her YouTube channel also contains statistics teaching videos that use her published methods to foster conceptual understanding of core concepts. 

Outside of work, Hilary loves to connect with nature and to connect to her voice by singing.

Selected Publications

Journal Articles

Amati F, Green J, Kitchin L, et al., 2023, Ethnicity as a predictor of outcomes of psychological therapies for anxiety and depression: a retrospective cohort analysis, Behavioural and Cognitive Psychotherapy, Vol:51, ISSN:1352-4658, Pages:164-173

O'Farrelly C, Barker B, Watt H, et al., 2021, A video-feedback parenting intervention to prevent enduring behaviour problems in at-risk children aged 12-36 months: the Healthy Start, Happy Start RCT, Health Technology Assessment, Vol:25, ISSN:1366-5278, Pages:1-+

Watt H, 2020, Reflections on modern methods: statistics education beyond “significance”: novel plain English interpretations to deepen understanding of statistics and to steer away from misinterpretations, International Journal of Epidemiology, Vol:49, ISSN:0300-5771, Pages:2083-2088


Watt H, 2023, Teaching Conceptual Understanding of p-Values and of ConfidenceIntervals, Whilst Steering Away from Common Misinterpretations., Burwalls teaching medical statistics., Editor(s): Medeiros Mirra, Farnell


Watt H, Leedham-Green K, Farnell D, et al., Round Table: Discussion on choice of confidence interval (CI) “definition”, Burwalls 2022: Annual meeting for teachers of statistics in medicine and allied health sciences

Watt H, Leedham-Green K, Farnell D, et al., Denying knowledge of differences amongst subjects when p>0.05; people are less likely to make this error when their p-value interpretation includes “population”, Royal Statistical Society 2022 International conference

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