Which calculation describes the difference between a measurement and the true value?

The calculation that describes the difference between a measurement and the true value is the Root Mean Squared Error (RMS). This metric quantifies the accuracy of a measurement by comparing the predicted or measured values to the true values. RMS is computed by taking the square root of the average of the squared differences (errors) between the predicted values and the actual values. This approach effectively emphasizes larger errors due to the squaring of the differences, providing a reliable estimation of the overall error across a dataset.

Using RMS helps in understanding the magnitude of errors and is particularly useful in various fields, including geospatial analysis, where precision is crucial. It provides a single number that summarizes the performance of a model or measurement system, offering insights into the accuracy and reliability of results in a straightforward manner.

The other metrics mentioned may focus on different aspects of error and variability. For instance, Mean Absolute Error measures average error in absolute terms, without emphasizing larger discrepancies. Standard Deviation provides a measure of variability within a dataset rather than a comparison to a true value. Residual Analysis examines errors in a model's predictions but does not provide a direct calculation of the difference from a known true value.