Visualizing False Positives In Broad Screening

AlejoHausner writes "To find one terrorist in 3000 people, using a screen that works 90% of the time, you'll end up detaining 300 people, one of whom might be your target. A BBC article asks for an effective way to communicate this clearly. 'Screening for HIV with 99.9% accuracy? Switch it around. Think also about screening the millions of non-HIV people and being wrong about one person in every 1,000.' The problem is important in any area where a less-than-perfect screen is used to detect a rare event in a population. As a recent NYTimes story notes, widespread screening for cancers (except for maybe colon cancer) does more harm than good. How can this counter-intuitive fact be communicated effectively to people unschooled in statistics?"

Comments

Re:Math ftl

[ShadowRangerRIT]

Wow. Way to illustrate the point. Remember, terrorists are roughly zero percent of the population (at least, of the population going on plane trips in the U.S./U.K.). Odds are, at most one of those 3000 actually is a terrorist. So if it is 90% accurate in identifying terrorist vs. non-terrorist (and vice versa), then 10% of the non-terrorists will be identified as terrorists (or ~300), while the 0-1 terrorists will be missed 10% of the time. And of course, since you don't know for sure if there was a terrorist in the group, an in-depth search of the 300 will usually be a waste of time.