For about a month, I’ve been tracking my sleep habits using two different sensors.

One of these sensors is tucked under my mattress, plugged into the wall, and connected to my apartment’s WiFi. It keeps track of movement, and seems to be fairly sensitive: it gets a lot of its most vital data from my breathing, as detected through the mattress. It then packs that data together each morning, sends it away to a processor farm somewhere, and spits out its findings to an app on my phone about thirty minutes after I’ve woken up and started my day.

The other sensor is built into my fitness tracker: a simple gizmo that I bought primarily to help me keep tabs on my heart rate while I’m running, but which has also, as a byproduct, allowed me to discover that my heart rate doesn’t get as high as I thought when I perform my normal, nightly workout routine, but does get quite high when I play guitar. This little wrist-mounted gadget uses similar data to tell me how I slept the night before, and that data is also presented most cohesively through another app on my phone.

What’s been particularly fascinating to me has been the difference in reporting between these two devices. It took a few weeks to determine the specifics, but I’ve come to realize that the fitness tracker seems to bundle all the time from when I get into bed until I get out of bed in the morning as my “sleep” period, while the mattress-based sensor is more granular, waiting until I actually fall asleep before it starts sorting out whether I’m in a light, deep, or REM sleep state.

Part of the issue here is that I usually read for anywhere from fifteen-minutes to a few hours while in bed, before actually sleeping. The lights out, the light on my Kindle turned down low, this is one of my favorite routines, and one I’ve been enjoying for years.

I’ll sometimes check my phone—also turned to a low-light, nighttime setting—to check when I get into bed and when I set down my book. But by comparing these two sensors’ data, I’ve been able to derive a more accurate outline of my timing for these portions of my night. When I lay down, how long I read, when I decide it’s time to sleep, and how long it takes me to conk out for the night. From there, I also know how long it takes me to segue into a deeper sleep, if I wake up during the night, when I typically wake up in the morning, and how long, in total, I’ve slept each night, each week, and each month.

All of this information, unto itself, is interesting. Especially if you’re into little lifestyle tweaks and figuring out ways to optimize aspects of your day by experimenting with different ways of doing things.

So this information has already been useful to me. Heart rate monitors worn on your wrist are imperfect by default, but by combining that data with other data, collected in other ways, it becomes more valuable. Such monitors also come with important privacy tradeoffs, but for my purposes at least, those tradeoffs are currently worthwhile.

Perhaps the most valuable aspect of all this data-gathering, though, has been slamming my combined nightly sleep results up against my personal perception of how I slept the night before.

Each morning, before checking my little data-collection apps to see how the machines think I did, I’ll take a moment to self-assess; to consciously think, “Okay, well how did that go?”

I’ve found that, over the course of the month, my estimates have gotten better. Because I’ve had this more objective information available, I’ve been able to combine the feeling of being especially well-rested—or especially not well-rested—with the quantity of hours slept, or the deepness or lightness of my sleep, or with noises or internal conflict waking me up throughout the night. Having the objectivity to anchor my subjectivity, in other words, has helped me increase the quality of my subjective interpretation.

One of the major flaws found in the world of sociology is that much of the information collected is based on self-reported data points. The sociologist asks someone a question, and the person on the other end of that question answers to the best of their ability. That information is then used to ascertain larger truths.

In some cases this isn’t much of an issue, but quite often we are terrible at gauging things, at unaided self-assessment, and that includes things we’ve been doing for a very long time, like sleeping, like determining when we got to sleep, like determining how well we slept last night compared to some other random night the previous month.

There are a great many variables that can warp our perception of such things, and unfortunately we often make decisions based on that warped perception.

We tell ourselves that everything is fine, but in reality we’re feeling stressed and frantic and unwell. Had we better means of comparison, we might realize this. But being stuck in our heads, our bodies, the way we are, it’s nearly impossible to extricate our assessment from our subjective experience. Maybe things seem like they’re going well because we were less stressed, less frantic, less unwell-feeling yesterday than the week before. That doesn’t mean we couldn’t change things around to feel even better were we able to recognize our more expansive baselines.

Data, unto itself, can be interesting. You might even find it useful, in a geeky, experimental, “hey, that’s neat” sort of way.

But the real utility of collecting and crunching this data, in my opinion, comes from comparing it to our own subjective experiences and seeing what emerges from that combination. Using it to calibrate our perception of reality, of how we experience the world, and remaining skeptical of these results—none of these commercially available sensors are perfect—while also remaining open to the possibility that what we’ve been construing as reality is, in fact, only part of the big picture. Using such an approach, we might, with a few small adjustments here and there, get more out of life than we would have previously thought possible.

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