Final answer:
On a target, several points in the same area indicate precision, the consistency of results. Points close to the bull's-eye indicate high accuracy as well. Clustering of points is a way to visually assess the quality of attempts or measurements.
Step-by-step explanation:
When several points on a target are found grouped in the same area, this indicates a level of precision in the measurements or attempts. Precision refers to the consistency and repeatability of results. For example, when considering a GPS system attempting to locate a restaurant, multiple attempts to pinpoint the location might yield results that are close together (high precision), or they might be spread out (low precision). If the points are also close to the intended target, the center of the bull's-eye, it indicates that the measurements are also accurate (high accuracy). If they are far from the target, this would be considered low accuracy. Thus, the clustering of points near the center of the target indicates both high precision and accuracy, while clustering far from the center but close to each other indicates high precision but low accuracy.
Consider the scenario where darts are thrown at a dartboard with the bull's-eye representing the true value. A dart hitting the bull's-eye would be both precise and accurate if consistently occurring. Different outcomes on the target, as seen in various figures, represent different combinations of precision and accuracy. Clustering of points is a visual representation that helps us determine the quality of the attempts or measurements. Points clustered closely together but far from the target suggest that the method is repeatable but not correctly aligned with the true value, while diverse spread close to the target suggests random but average accurate attempts.