Looking for a statistical term close to "precision"

The source https://en.wikipedia.org/wiki/Accuracy_and_precision says that in statistics "precision" is understood to be a measure of statistical variability within samples. The lower the variability within the sample, the higher the "precision." That's okay. It is a technical term. But I wonder if there a standard term in statistics for this other thing:

Suppose one study of the heights to students in various high schools measures the heights to the nearest inch, and another measures them to the nearest millimeter. (I intentionally use an implausibly small unit here. A person's head rises and falls more than one millimeter just by breathing.) In a colloquial sense the second study reports "more precise" numbers than the first -- even if those numbers are no more reliable and the "precision" is illusory.

Is there a standard term in statistics to describe how closely a given quantity is being described -- the way heights to the nearest millimeter are given "more closely" than heights to the nearest inch?

• Interesting. Kind of like bin size but not quite. Nov 2, 2019 at 22:48
• Some non-technical terms include “more fine-grained” (which has a positive connotation) or “more digits” / “more digits after the decimal point” (which has a negative connotation, at least for anyone influenced by Tufte) Nov 3, 2019 at 11:47
• I would call it the "graduation" or the "minimum increment" of the measurement Dec 17, 2019 at 14:06
• Another term that might make sense but is less self-explanatory is "nominal precision" Dec 17, 2019 at 14:09

I have heard statisticians and data scientists use the word 'granularity' to express the idea you are looking for. Here is a quote from the dedicated Wikipedia article: 'The granularity of data refers to the size in which data fields are sub-divided'. The article also gives an enlightening example of proper usage: 'A kilometer broken into centimeters has finer granularity than a kilometer broken into meters'. Here is the link to the article: https://en.wikipedia.org/wiki/Granularity#Data_granularity.

I don't think there is perfect standard terminology, but I think we can reason our way to a viable option.

Let us imagine that we are using a person's height as a feature for a classification problem, like predicting health outcomes or athletic performance. How do we expect the model to change if we are able to measure height accurately to within a millimeter rather than just an inch? It is natural to expect the model accuracy to improve, but this could depend on the details of how the model is fit to data - using millimeters could cause it to overfit, for instance.

I would argue that the direct, first-order effect of using millimeters instead of inches is that the model will likely become more discriminative: the probabilities that it assigns to different classes will be more concentrated in a small number of classes with the more refined units. So it makes sense to me to apply this label to the units of measurement and say that millimeters are more "discriminative" than inches. If we need a noun like "precision" then I guess it would be "discriminativity", though I'm not sure that's actually a word.

This choice of terminology has a number of nice properties. It has a generally positive connotation (discriminative models are usually desirable), though the statistically literate reader will be cautious about overfitting and wonder how much of an effect making certain measurements more discriminative will have on the statistical power of the overall study. This is probably how the reader should approach a study which measures a person's height in millimeters rather than inches, so it fits your example well, at least.

• I think terms like scale, refinement, and sensitivity might come closer, especially when understood as measurement scale, measurement refinement, and measurement sensitivity. Gerhard "Within An Order Of Magnitude" Paseman, 2019.12.02. Dec 3, 2019 at 5:45
• I considered both "refinement" and "sensitivity", but I was worried about collisions with other terms in statistics. Refinement is often used to refer to restricting from a type to a subtype, like from $H \in \mathbb{N}$ to $\{H \in \mathbb{N}, H < 100 \}$. I didn't like sensitivity for two reasons: 1. sensitivity is used in statistics as a synonym for recall, which isn't the right meaning here, and 2. the other connotation is something like "risk of greater error", whereas the difference here is that millimeters can measure error more accurately than inches, not that the error is greater. Dec 3, 2019 at 7:23
• "Scale" is a good term here, though it is awkward to use for comparisons - does "more scale" mean larger or smaller units? It also doesn't hint at the strengths and weaknesses of different choices the way "discriminative" does. Dec 3, 2019 at 7:27
• The connotation will be negative for many people, who associate discrimination with negative decisions based on gender or race. Dec 3, 2019 at 10:17
• That could indeed be a worthwhile consideration, but in this case "discriminative" is already a statistical term of art, and I am only proposing that we extend its meaning in a modest way. Dec 3, 2019 at 14:18