Thoughtful Gear Substitutions for Your Climbing, Mountaineering Backpacking, Hiking, or Camping
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Through the course of my professional life, I have worked in health care analytics. There is, obviously, a certain amount of reliance on scientific method that comes with interpreting the story behind the data. While I’m not a hard scientist, like a biologist or chemist, I still need to know how that data was gathered, meaning I need to know if we are measuring an experiment, natural phenomenon, or even something else.
This is where the scientific method comes in. In health care, you will often hear of the “double-blind randomized control trial.” Fancy words. What does it mean? It means that - lets say out of two thousand people - one thousand of them are given the new drug or treatment or whatever and the other one thousand are not given the new treatment. Further, the thousand that get the new treatment are selected at random, so that over a large enough group of people you will get a thousand people that look something like the population, in general, rather than giving it only to women or only to Asian ethnicities, or whatever.
And further still, it is “double-blind,” meaning that neither the people receive the treatment nor giving the treatment know if any one dose or injection or whatever is the real, new treatment or a fake (placebo). So, no one is behaving differently than they otherwise would either before, during, or after treatment because they don’t know if they really got the treatment.
What is going on, here? Well, they are trying to limit all the variables from changing except for only one: the new treatment is either given or it isn’t. Everything else is the same.
The other big part of my career has been in product development. I’ve worked for companies that have build analytic software, helping health care companies do analyses that they have to repeat over and over again in a more automated way. In product development, you “run experiments,” too.
For example, you might have a slider that allows you to expand or shrink the date range of the data you want to use for the analysis; you slide to the left, and you add in data from further back in history; you slide to the right and you shrink the date range to only include very recent data. But you could also do the same thing with a calendar, allowing the user to click on a specific starting date and specific end date of the range he or she wants. So, you could release two versions, pushing the slider version to some users (randomly chosen) and the calendar version to another set of users.
What you aren’t doing, though, is releasing two completely different pieces of software with other differences between them. Again, you are making a small experiment: does this slider work better than this calendar or vice-versa. You aren’t running a big experiment: how do I make the best software, overall.
I think it was my background in these two areas, health care analytics and product development, that led me to rather naturally approach my process for making gear changes in the same way. I run small experiments, changing one thing at a time and trying to deduce if the change has had a positive impact, negative impact, or maybe no impact at all.
I mention in the video that I have another post that is basically a case study of this approach: fiddling with my sleep system until I could find equipment that would allow me to sleep well in below zero (Fahrenheit, -18 Celsius) that was under three pounds (4.6 kilograms). If you want to go through that case study in more detail, you can follow this link.
In addition, you see a lot of the equipment I have come to use as that cold weather sleep system. If you are maybe interested in learning more about those items, here are the links.
Therm-a-rest NeoAir XTherm inflatable sleeping pad