Winter Stove Test Results Will Surprise You!
(This post may contain affiliate links, which means that if you click on one of the product links and make a purchase, I’ll receive a small commission at no extra cost to you. This helps support the channel and allows us to continue to make videos like this. Thank you for the support!)
I like data. But I’m not “data driven”… at least not in the absolute sense.
In my professional life, I used to do speaking at conferences, and one of my repeating talks was about how most of us are likely less data driven than we think. Keep in mind these were professional conferences, so speaking to IT an analytics professionals who, typically, pride themselves on going where the data points.
But let’s unpack that - just for a moment. (I will bring this around to stoves in a minute.)
If you are the manager of a business process, maybe your team is a call center, and you handle service recovery, you might have certain questions that can be informed by data. Maybe you want to know a ranked-hierarchy of places in the customer journey that lead to a service call. So, you ask your analytics team to put something together. They do. You decide to reach out to your business partners who “own” those steps in the customer journey to begin a problem identification and resolution effort so that service recovery calls can be avoided.
Did we make a data-based decision? Sure. I think so. But not in the absolute sense. Really what happens is this manager has learned to trust the analytic team and have taken it - based on that trust and some explanation - that good methods were used, mistakes were not made, and the results are true. Really what we’ve done is trust the messengers, not the message… because we didn’t actually see the message. We saw the polished summary.
Well, what if the manager does the analysis herself? Well, no we just kick the trust can up stream. Was the data quality good? Has the data been normalized and made fit for use? … and so on.
Is that more data driven. I guess, sure. So, I think of being data driven as being on a continuum, not a binary “you are” or “you aren’t.”
The practical reality is that we deal with so much data, now, that in most circumstances (at least in business) we can’t possibly do the gathering, and the aggregating, and the making ready for analysis, and the analysis, and the concluding, and the convincing, and the decision making. No, rather we farm out each of these steps to different teams or individuals… or even to software.
Okay… stoves.
All the above doesn’t mean data is worthless. To the contrary, I have made a career of working with data and trying to turn it into recommendations for things to do and not do. Data is incredibly helpful, and I like using it.
Hence this stove test. I could make a decision for the stove I want to take based on specifications: stove weight, volume as it sits in my pack, listed boil times, etc. But there is next-level data that matters to me. What about the fuel efficiency of those boil times? That factors into weight and volume as I can bring more or less fuel. Do I need to bring a specific pot (some stoves work better with narrower or wider pots based on flame distribution). How does the stove perform in the wind? What about if I add a wind guard (which adds to the weight). The things these have in common is that they are all empirical questions, meaning the questions can be tested an answered rather than just hypothesized about.
So, again, tests.
The video gets deeper into the efficiency question by testing eight stoves (two of each “style” of stove across more minimalist, integrated, inverted-canister, and liquid fuel). I’m trying to get at the idea that efficiency impacts weight in a major way when we are having to turn snow into water, because the process is so fuel intensive. What might start out as the “light” option may not prove to end up that way once we have to take more fuel along.
It turns out that we can see a trajectory of heavier stoves making more sense when we are out on longer-duration trips, but for shortish trips, I was actually surprised by the stoves that proved to be “efficient enough” to still be useable in harsher, winter conditions.
But each of the stoves in the test did have a “virtue” to them. None of them were crap that I would never use. Heck, I bought them and use them all… just tailored to the trip I am having and therefore the stove characteristics I want for that trip.
For those who are interested in learning more about each stove (maybe after watching this video), you can find them by following each link:
If you are curious about what I choose most often, by stove class, I prefer the MSR Pocket Rocket Deluxe, the Jetboil Mini Mo, the Optimus Vega, and MSR XGK EX. I probably use the Mini Mo most frequently, overall. But, again, if I were cooking for eight people, I probably wouldn’t be choosing the Mini Mo over something like the XGK. Or if I wanted more control while cooking, the Dragonfly can simmer.
But, back to my point from above: Like everyone else, I am not perfectly data driven, either. I can gather as much data as I want, but I will run into some constraining realities. First, that data will always be imperfect; I cannot precisely mimic the circumstances of use for every situation in which I will use the stove. Second - and more importantly I think - even with all the data I can gather, nothing about the data can tell me which data to care about.
I might care about weight a lot, because I go alpine climbing. But I probably don’t care about weight as much as a backpacker because I am melting snow for water and the backpacker (let’s say) is not. So, efficiency might be higher on my scale and weight higher on the backpacker’s scale. Or maybe you are care camping, and you really care about that simmer control I mentioned - which is more of a existing or non-existing feature set.
In those talks I used to give, I used to use an (overly simple) hypothetical: You are running for public office. You put a poll in the field asking your potential constituents about your education policy. It comes back that 45% agree with your policy and 55% disagree. What do you do?
Well, the answer depends on a personal value: do you think you should lead your constituents, in which case you might try to convince the 55% who disagree that your policy is a better way to go, or do you think you should represent your constituents, in which case you might change your policy?
Yes, overly simple. But it illustrates the point. Data can only help us make decisions when looked at through the lens of what we value, whether that be leadership versus representation or weight versus efficiency.