Gerd Gigerenzer
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A new drug reduces the risk of heart attacks by 40%. Shark attacks are up by a factor of two. Drinking a liter of soda per day doubles your chance of developing cancer.

These are all examples of relative risk, a common way risk is presented in news articles. Risk evaluation is a complicated tangle of statistical thinking and personal preference. One common stumbling block is the difference between relative risks like these and what are called absolute risks.

Risk is the likelihood that an event will occur. It can be expressed as either a percentage— for example, that heart attacks occur in 11% of men between the ages of 60 and 79— or as a rate— that one in two million divers along Australia’s western coast will suffer a fatal shark bite each year. These numbers express the absolute risk of heart attacks and shark attacks in these groups. Changes in risk can be expressed in relative or absolute terms. For example, a review in 2009 found that mammography screenings reduced the number of breast cancer deaths from five women in one thousand to four. The absolute risk reduction was about .1%. But the relative risk reduction from 5 cases of cancer mortality to four is 20%. Based on reports of this higher number, people overestimated the impact of screening.

To see why the difference between the two ways of expressing risk matters, let’s consider the hypothetical example of a drug that reduces heart attack risk by 40%. Imagine that out of a group of 1,000 people who didn’t take the new drug, 10 would have heart attacks. The absolute risk is 10 out of 1,000, or 1%. If a similar group of 1,000 people did take the drug, the number of heart attacks would be six. In other words, the drug could prevent four out of ten heart attacks— a relative risk reduction of 40%. Meanwhile, the absolute risk only dropped from 1% to 0.6%— but the 40% relative risk decrease sounds a lot more significant.

Surely preventing even a handful of heart attacks, or any other negative outcome, is worthwhile— isn’t it? Not necessarily. The problem is that choices that reduce some risks can put you in the path of others. Suppose the heart-attack drug caused cancer in one half of 1% of patients. In our group of 1,000 people, four heart attacks would be prevented by taking the drug, but there would be five new cases of cancer. The relative reduction in heart attack risk sounds substantial and the absolute risk of cancer sounds small, but they work out to about the same number of cases.

In real life, everyone’s individual evaluation of risk will vary depending on their personal circumstances. If you know you have a family history of heart disease you might be more strongly motivated to take a medication that would lower your heart-attack risk, even knowing it provided only a small reduction in absolute risk. Sometimes, we have to decide between exposing ourselves to risks that aren’t directly comparable. If, for example, the heart attack drug carried a higher risk of a debilitating, but not life-threatening, side effect like migraines rather than cancer, our evaluation of whether that risk is worth taking might change. And sometimes there isn’t necessarily a correct choice: some might say even a minuscule risk of shark attack is worth avoiding, because all you’d miss out on is an ocean swim, while others wouldn’t even consider skipping a swim to avoid an objectively tiny risk of shark attack. For all these reasons, risk evaluation is tricky at baseline, and reporting on risk can be misleading, especially when it shares some numbers in absolute terms and others in relative terms. Understanding how these measures work will help you cut through some of the confusion and better evaluate risk.