Final answer:
An example of data that is accurate but not precise might be measurements of a known length that are close to the true value (accurate), yet do not fall close to each other (imprecise). Accuracy is proximity to the true value, while precision refers to the consistency of multiple measurements. Uncertainties should always be considered when assessing such data.
Step-by-step explanation:
An example of data that is accurate but not precise is when individual measurements are close to the expected value (accurate) but not close to each other (lacking precision). Imagine you are measuring the length of a standard sheet of computer paper that is known to be 11.0 inches long. If your measurements are 10.9 in, 11.1 in, and 11.2 in, they are accurate because they are close to the true value, but they are not precise because they are not very close to each other.
Accuracy is determined by how close a measurement is to the correct value for that measurement, whereas precision refers to how close multiple measurements are to one another. A common example is in a classroom setting where a teacher calculates the average exam score for the class. The average score represents a parameter, which is a value that includes data from the entire population (in this case, all the students in the class).
When assessing data from graphs or experiments, always consider the uncertainty and whether the measurements fall within acceptable data extraction uncertainties.