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The world is awash with misinformation, much of which has the appearance of being 'scientific': from striking graphs shared widely on social media, to algorithms used for decision-making by governments and self-serving claims made by companies. This makes the ability to detect various forms of 'bad science' – from misleading data visualizations to algorithms with bias 'baked in' – a critical skill for any citizen. Being able to reject bad apples in the 'marketplace of ideas' is vital not just for the autonomy of individual decision-making, but for justice and democracy.
This subject will explore historical and contemporary examples of dubious, misleading and junk science, with topics including common statistical traps and tricks, conflicts of interest, the role of confirmation bias and cultural identity in the consumption of scientific claims, and the pitfalls of big data.
The teaching delivery is through highly interactive and engaging 'lectorials', enabling students from all degrees to contribute their diverse perspectives, sharpen their radar for bad science, and think though the social and ethical implications.
Intended learning outcomes
- Critically analyse data, models and knowledge production;
- Identify the psychological characteristics that make us susceptible to 'bad science';
- Collaboratively analyse the ethical and social implications of 'bad science', and
- Respect for viewpoint diversity and how to demonstrate respect for it, and benefit from it.
- Critical thinking;
- Effective written academic and non-academic communication skills;
- Constructive collaboration;
- Openness to diverse viewpoints, and
- Ethical awareness.
Last updated: 21 January 2020