My last post was surprisingly popular—and not just among people who know me personally. I even managed to pick up a few new followers, whom I’m afraid will be put off a bit when they discover travel writing isn’t aligned the usual subjects of this blog (but hopefully not!).
Anyway, as you may or may not recall, the last post incorporated a graph of the distance I’d walked the days before, during, and after various legs of my trip through Italy:
In the graph’s caption, I glibly blamed my apparent sedentarism on my office job and commute. I like to think of myself as a decently fit person, you see. Surely, I reasoned, my desk job must be impeding an otherwise active lifestyle. I mean, I have a standing desk—clearly I’m a man who values his physical fitness.
It occurred to my a few days later that my hypothesis was actually pretty testable: if work and commuting were really to blame, my weekends should be significantly more active (measured by distance walked/run) than average. Apple has, for some reason, elected to make exporting health data from iPhones an incredibly difficult process, so, with the zeal of an intern, I manually entered 242 days worth of mileage, attempting to evidence my claim.
Looking back, my naiveté was almost cute. In the era of “binge-watching,” I really believed myself exceptional.
The raw data is pretty depressing, I must say. The mean distance walked is 1.54 miles. But the data is right-skewed, meaning outliers on the upper end of the distribution are pulling the mean higher. (The median distance walked over this period is a shockingly low .985 miles.) It’s also telling that the distribution isn’t bimodal, which would indicate two distinct populations—in the case of my hypothesis, weekdays and weekends.
I could have quit here, but I’ve touched on the importance of publishing negative results before and therefore had a cross to bear. To make the data set more normal, I removed outliers (in this case, all values greater than 3.73 miles) and used a square-root transformation:
The means of our new, outlier-free population and the “weekend” sample (n=61) are, respectively, 1.023² miles and 1.046² miles, and the population standard deviation is .377² miles. At the .95 confidence interval, the sample would have to have a mean of about 1.106² miles to be statistically higher than the average.
It is with great shame that I reject the alternate hypothesis. And I do hereby humbly apologize to office life for blaming it for what is clearly a personal shortcoming.
A few caveats, in case my health insurance provider is reading:
- I do exercise most days before work. But mostly pull-ups, lunges, and other anaerobic stuff. I only run sporadically—and when I do, I don’t always bring my phone with me.
- I can’t vouch for the accuracy of the iPhone’s pedometer. Anecdotally, I’ve heard it isn’t great, and light research confirms it has trouble measuring steps under some common conditions, like being held or kept in a backpack.
- The combination of the above suggests iPhone health data offers a convenient but incomplete metric to assess one’s activity. For example, July 31, a day my phone credits me with walking 4.7 miles, also happens to be a day I went for a 30-mile bike ride, for example.
- Including Fridays in the “weekend” sample raises the mean distance slightly, to 1.08 miles, but still not enough to achieve statistical significance.
- Uhh, I will try to do better.