# How would you attempt to distinguish measurement error from a true distribution but no “true” singular value?

I am self-taught amateur in statistics and I have confusion is based calculations with error-in variables with the added object being measured having a no "true" value but a "true" range.

For me, the confusion usually comes up from doing statistics with calculated data particularly in the sciences. In the sciences, observations are limited by the measuring devices used. Often, we use these observations to calculate a meaningful metric that has a "true" value to analyze. Furthermore, statistics is primarily used to distinguish measuring error with true differences in the calculated metric.

To my knowledge in statistical terms, the sciences use many error-in variables to try to identify true values and difference in experiments. However, I only know of solutions to try to tease out the true value. To my main question, how would you attempt to distinguish measurement error with a true distribution but no "true" singular value? What branch of statistics would this encompass? Where could I get resources to learn about this side of statistics?