Skeptics on the Fringe: Badgers and TB: black and white or 50 shades of grey?

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What does the evidence say? And why do different groups look at the same evidence but reach different conclusions?

Dr Graham Smith, Co-organiser York Skeptics and Head of Wildlife Epidemiology and Modelling at the Animal and Plant Health Agency, takes a skeptical look the Bovine TB crisis and the response to it by the government and the different advocacy and pressure groups.

Bovine tuberculosis costs us about £100m as British taxpayers each year, as well as the cost to individual farmers when their cattle contract TB. Badgers also catch TB, and badger management is a highly divisive issue. England, Wales, Northern Ireland and the Irish Republic all manage the issue differently but mostly rely on a similar evidence base.

Graham will start by discussing some of the reasons why we all interpret data differently. He then looks at how government treats evidence, and the data relating to bovine TB. He presents the facts of the issue and invites us in the audience to make up our own minds and test our conclusions against the biases in the first part of the talk.

Dr Graham Smith has spent over 20 years working in scientific research for the Department for Environment Food & Rural Affairs collecting, analysing and interpreting data on wildlife problems. He has worked on a number of disease issues including many years on badgers and bovine tuberculosis. His main work concentrates on producing computer models to help decision makers interpret the available data and predict the consequences of different option. This relies heavily on understanding the uncertainty associated with all parts of the system.

When he emailed us he added :

I also have a long standing interest in critical thinking and the interpretation of available data. Why is it we can all see the same data but draw different conclusions from it? Due to this interest I have been involved with York Skeptics since the first meeting, and regularly give talks on critical thinking and the interpretation of evidence.

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