Pedestrian traffic fatalitiesUS nutrition and energy bar retail sales
It is a curious fact, and one that most people find mildly irritating when pointed out, that Americans have been dying under cars at precisely the same rate they have been purchasing compressed oats and protein powder in foil packets for the past eighteen years. One might expect these two phenomena to have absolutely nothing to do with each other, rather like correlating the price of tea in China with the number of left-handed plumbers in Nebraska, and yet here we are. The universe, it seems, is not above a bit of cosmic trolling.
What we're probably watching is the shadow of something far more mundane: both measures track population growth and economic vitality in the same direction. As more Americans drove more cars on more roads during periods of economic expansion (2005-2010, 2015-2022), fatalities climbed; in those same periods, the energy bar industry—which barely existed in 2000—exploded into a $7 billion enterprise as gym membership soared and wellness became genuinely fashionable. Both trends are really just saying the same thing: more people, more activity, more everything. Think of it this way: if you add roughly 25 million people to the American population while simultaneously convincing them to jog before work, you've just increased your denominator for traffic deaths and your addressable market for granola bars in one fell swoop.
The real insight here is not that snack bars are killing pedestrians, but rather that we are all pattern-seeking creatures desperate to find a story in any two datasets that move in concert. We have fooled ourselves into thinking that pedestrian fatalities and nutrition bar sales share some hidden causal thread when they are really just two different instruments playing along to the same economic heartbeat. The patterns we find are usually just echoes of the same underlying rhythm.
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Want to learn more about why correlations like “Pedestrian traffic fatalities” vs “US nutrition and energy bar retail sales” don't prove causation? Read our guide to statistical thinking.