A few examples:
- what tradeoffs did companies examine before concluding to move to microservices from monoliths? Which companies decided not to? Why?
- When Angular.js first came out, what made companies adopt it? What were they using before?
- Why did everyone leave PHP?
- What made Postgres become popular? What were they using before? Who decided to stick with their original tech?
Does there exist a chart with maybe
- industries on the x axis (healthcare, transportation, ecommerce, finance...) and
- technologies on the y axis (Next.js, React, Angular, Postgres, AWS, MySQL, Jenkins, etc.), and
- each cell is did this industry adopt this technology in general? How long did it take? Why not initially? What changed?
- what motivated you to learn to draw
- what you were interested in drawing
- what your biggest roadblock/weakness to getting better was
- how many times you've tried to learn
Usually there's a sweet spot between two extremes.
Examples:
- Bias-Variance trade off from statistics and machine learning
- Freedom vs Security from philosophy
- Explore vs Exploit from computer science
I'd love to hear some other examples from e.g. anthropology, chemistry, physics, bio, marketing, etc.
What I'm really hoping is that this happens as much as possible:
- person from field A posts
- person from field B replies to field A post: hey! that's kind of like X from field B
- person from field C comments: Or like Y from field C!
etc
I'm interested in tricks, tools, or mental models to help answer subjective questions with accuracy and precision (and eliminate biases).
For example, how do you turn the following questions into insightful, actionable data?
- How much does this hurt?
- How much do you like this product?
- What are you feeling right now?
Backstory:
I'm 28 and I developed tinnitus out of nowhere a few months ago (I don't go to concerts, I don't sit in server rooms, etc).
It's "mild" to "moderate" right now, I'm terrified of it getting "worse" as I age. I'd like to track it over time (months, years) to see if it gets quantitatively worse.
The issue: it's really hard to
1) control for all environmental variables (sleep, exercise, diet, etc), and
2) answer the question "how bad is it right now?" with consistency (i.e. however bad it is today, I will also give that same response when it is also that bad n days from now)
At first, I recorded (twice daily) my answer to the question:
Scale 1-10 how bad is it right now?
But then I realized - what if it's just not there at some point? I could do mental gymnastics and normalize it so that 1 corresponds to not there but it seemed easier to add 0 to make it 0-10.
But then, when the question became
Scale 0-10 how bad is it right now?
I realized I had no visceral, high-fidelity understanding of what a 3 was, nor a 4, etc.
So I shrank it to 0-5
(I think I'm sacrificing granularity for precision?)
Now, it's easier to assign a number to a subjective experience, but it got me thinking:
This is interesting from a customer / product research perspective and from a scientific research prospective. How do you phrase survey questions to maximize accuracy and precision? Is there a tradeoff? I'd love resource recommendations for studying this idea.