Normalize across groups, individuals or population? I would like to compare sensors of a manufacturer A with those of a manufacturer B. As they provide different measurement magnitudes, I want to scale the variables such that it makes more sense to compare them:

But I'm not sure how exactly this should be done: Shall I scale the sensors

*

*individually

*among the manufacturers

*or across the population?

The latter probably makes no sense but the others?
The upper four belong to manufacturer A and the lower four to manufacturer B.
This is how it looks when scaling per manufacturer:

 A: First, my understanding of what the product actually does, to quote a reference:

A resistive sensor is a transducer or electromechanical device that converts a mechanical change such as displacement into an electrical signal that can be monitored after conditioning. They are commonly used in instrumentation.

Second, assume you can construct a representative population of displacements that could be generated in practice or that comprise areas of interest (appropriately repeated to reflect relative importance).
For each known value of displacement in the constructed test population, obtain the generated electrical signal (whose precise value is known) by the manufacturer's product.
You now have a basis to compare manufacturers in performance, or how to weight each manufacturer to construct a super population for select goals.
A: Doesn't the main objective of normalization imply that you should normalize your whole dataset? Meaning, assuming you have the same variables for both manufacturers, you create a dataset with all your observations, and scale it accordingly?
I think it would be useless to scale the data per manufacturer; to make sense, I believe you need to scale all the observations in your dataset simultaneously.
