Recently ABC News (and others) ran a story about how doctors in Sweden discovered an increase in heart attacks every year when the clocks spring forward and similarly a decrease in heart attacks when the clocks fall back. What’s surprising about ABC’s coverage is that the graphics they ran with the story were completely irrelevant to the issue.
This story is a natural for a data graphic. Indeed most folks probably drew a mental picture similar to the one below, clearly showing an increase in heart attacks the week after the switch to daylight savings time and clearly showing a decrease the week after the switch back. (Many might have thought the pop was bigger, but the image below is scaled to represent a 5% increase over the average trend.)
Any single year may not have been so clear cut. The story said that the change in heart attacks was about 5%, but did not mention what the normal variation from day to day was. The sample above reflects a 1% daily variation with a 5% variation on the daylight savings time change. The difference is easy to see.
It could be that the daily variation was also on the order of 5% making the annual DST change harder to see in any given year. See for example the image below.
However given a choice between informative but idealized graphic (the first chart above) or useless and irrelevant graphic (what ABC ran with), which would you use? Which would you rather see?
Finally even if the daily variation was the same as the annual DST variation, a simple and accurate graphic could have been made. If every year the switch to DST increases heart attacks by 5% then over a period of several years the trend should become clear. Imagine, for example, that every day you flip a coin, but that each March 31st that coin lands heads up. On any given year it is impossible to detect this effect, but after 10 years, you should notice that it lands heads up 5 times on March 30, 5 times on April 1, and all 10 times on March 31; a bizarre but detectable pattern. Similarly the simulation below shows this same averaging effect over a 10 year period. (Click on the image to be taken to the simulation.)
The top part of the simulation is a typical year’s data, the bottom part is the average of all the years so far. Let the simulation run for a few simulated years and the change in behavior across daylight saving times boundaries is easy to see.
Note that after a few simulated years of running the average graphic matches up well with the original “minds eye” version above. ABC news could have ran an actual 1-year graphic if that looked like chart #1 above or it could have run a several year average graphic if the 1-year looked like chart #2. Either way, they could have ran a graphic that conveyed information instead of eye-candy to accompany the spoken words.