When measuring a quantity, what indicates that data is precise?

Data is considered precise when the measurements or results cluster closely around a central value, indicating a high degree of repeatability and consistency in the measurements taken. Precision refers to the degree to which repeated measurements under unchanged conditions show the same results.

When values are closely grouped together, it suggests that the measurement process is stable and reliable, allowing for small variations due to systematic errors or random fluctuations. This clustering demonstrates that the methodology used is effective in yielding consistent results, which is a crucial aspect of data precision.

In contrast, when results are widely varying or spread out from a central value, as indicated in the incorrect options, it suggests a lack of precision. Measures that are either very close to the actual value or consistently high or low do not necessarily reflect precision; these factors primarily relate to accuracy rather than precision. Thus, option C effectively captures the essence of what it means for data to be precise.