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Sharing knowledge requires a precise language for each domain of evidence. The challenge is to reach balanced judgements across these domains in a world of uncertainties. For "evidence-informed" decisions, what can reveal the "significant" patterns that together determine optimal solutions for society?

Ian Diamond ("Quantitative easing", 18 October) describes a data skills gap in undergraduate social sciences. Quantiphobia is rife in academic, professional and policy circles.

Consider a difficult decision currently argued from entrenched positions. We love badgers, which have an important role in our ecosystem and folklore, but we also have a strong cultural memory of the damage tuberculosis inflicted on our recent forefathers. Should we kill badgers to prevent culling those cattle that react to tuberculin because they have come into contact with TB bacteria? These cattle might develop TB disease that poses a risk to wider animal and human health. What impact is this decision likely to have on rural society, food supply and economics?

To make a balanced decision from sources of evidence covering many domains requires fluency in quantitative thinking. To pool this knowledge demands a "Rosetta Stone": the common language of statistics. This is no optional add-on, it needs to be embedded in learning experiences across all disciplines.

Tim Moore, fellow of the Faculty of Public Health Medicine

Woody Caan, fellow of the Royal Society for Public Health