News Release

Statistical oversight could explain inconsistencies in nutritional research

Peer-Reviewed Publication

University of Leeds

The research, led by scientists at the University of Leeds and The Alan Turing Institute - The National Institute for data science and artificial intelligence - reveals that the standard and most common statistical approach to studying the relationship between food and health can give misleading and meaningless results. 

Lead author Georgia Tomova, a PhD researcher in the University of Leeds’ Institute for Data Analytics and The Alan Turing Institute, said: "These findings are relevant to everything we think we know about the effect of food on health.  

“It is well known that different nutritional studies tend to find different results. One week a food is apparently harmful and the next week it is apparently good for you." 

The researchers found that the widespread practice of statistically controlling, or allowing for, someone’s total energy intake can lead to dramatic changes in the interpretation of the results. 

Controlling for other foods eaten can then further skew the results, so that a harmful food appears beneficial or vice versa. 

Ms Tomova added: "Because of the big differences between individual studies, we tend to rely on review articles to provide an average estimate of whether, and to what extent, a particular food causes a particular health condition.  

“Unfortunately, because most studies have different approaches to controlling for the rest of the diet, it is likely that each study is estimating a very different quantity, making the 'average' rather meaningless.” 

The research, which was funded by The Alan Turing Institute, identified the problem by using new 'causal inference' methods, which were popularized by Judea Pearl, the author of “The Book of Why.” 

Senior author Dr Peter Tennant, Associate Professor of Health Data Science in Leeds’ School of Medicine explained: "When you cannot run an experiment, it is very difficult to determine whether, and to what extent, something causes something else.  

“That is why people say, 'correlation does not equal causation.' These new 'causal inference' methods promise to help us to identify causal effects from correlations, but in doing so they have also highlighted quite a few areas which we did not fully understand." 

The authors hope that this new research will help nutritional scientists to better understand the issues with inappropriately controlling for total energy intake and overall diet and gain a clearer understanding of the effects of the diet on health. 

Dr Tennant added: "Different studies can provide different estimates for a range of reasons but we think that this one statistical issue may explain a lot of the disagreement. Fortunately, this can be easily avoided in the future.” 


 Further information 

Contact University of Leeds press officer Kersti Mitchell via with media enquiries. 

University of Leeds  

The University of Leeds is one of the largest higher education institutions in the UK, with more than 38,000 students from more than 150 different countries. We are renowned globally for the quality of our teaching and research.  

We are a values-driven university, and we harness our expertise in research and education to help shape a better future for humanity, working through collaboration to tackle inequalities, achieve societal impact and drive change.   

The University is a member of the Russell Group of research-intensive universities, and plays a significant role in the Turing, Rosalind Franklin and Royce Institutes.   

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The Alan Turing Institute is the UK’s national institute for data science and artificial intelligence. 

The Institute is named in honour of Alan Turing, whose pioneering work in theoretical and applied mathematics, engineering and computing is considered to have laid the foundations for modern-day data science and artificial intelligence. The Institute’s goals are to undertake world-class research in data science and artificial intelligence, apply its research to real-world problems, drive economic impact and societal good, lead the training of a new generation of scientists, and shape the public conversation around data and algorithms. 



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