Welcome to Understanding Uncertainty

Welcome to the site that tries to make sense of chance, risk, luck, uncertainty and probability. Mathematics won't tell us what to do, but we think that understanding the numbers can help us deal with our own uncertainty and allow us to look critically at stories in the media.


Sex by Numbers, by David Spiegelhalter, is now available for purchase!

Details of reviews, interviews, (and errors) are all featured here.

Do you have a coincidence story? David Spiegelhalter is collecting them over at http://cambridgecoincidences.org.

Elsewhere on UnderstandingUncertainty:

About Us

What is this site?

This site is produced by the Winton programme for the public understanding of risk based in the Statistical Laboratory in the University of Cambridge. The aim is to help improve the way that uncertainty and risk are discussed in society, and show how probability and statistics can be both useful and entertaining! However we also acknowledge that uncertainty is not just a matter of working out numerical chances, and aim for an appropriate balance between qualitative and quantitative insights.

Future Plans

Future plans for web-site (june 2008)

  • Regular updates / RSS feed
  • Encourage contributions on topics such as climate change and electromagnetic fields
  • 'Novelty’ features: eg Poetry corner, quotes
  • Use narrative as much as possible - story-based
  • Contribute to Wikipedia when ready, also get linked from there.

Notes for Reviewers

This site is in an early stage of development, and we would welcome your comments on any aspect of the content and presentation. A few points you may wish to keep in mind:

Qualitative Concepts

These might include:

  • Precautionary principle
  • Evaluating Evidence
  • Psychology of risk behaviour
  • Neurophysiology of risk behaviour
  • Sociology of risk / Risk in society
  • Philosophy of risk
  • What is ‘probability’/ risk etc?
  • Risk communication
  • Teaching resources on risk/uncertainty

Quantitative Concepts

These might include:

  • Learning from data
  • Absolute and relative risks
  • Individual and ‘average’ risks
  • Bayes theorem
  • Regression-to-the-mean
  • Unexpected / rare events, coincidences
  • Trends
  • When to stop?
  • Ranking
  • Predictability


These might include:

  1. Lifestyle
    1. Sex
    2. Drugs
    3. Smoking
    4. Alcohol
  2. Childhood
    1. Accidents
    2. Violence
    3. Obesity
  3. Healthcare
    1. NICE approvals
    2. Vaccines (MMR etc)
    3. League tables
    4. Drug Safety
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