Accuracy: The closeness of results of observations, computations, or estimates of graphic map features to their true value or position on the ground.
Precision (Repeatability): The closeness with which measurements agree with each other.
Facts about Accuracy:
- True value is the theoretically correct or exact value of a quantity. The true value is elusive to us, and it cannot be reached considering our human limitations. True value is a matter relate to metaphysics.
- Accuracy is part of the map metrics that need to be included in the metadata of any geospatial dataset.
To illustrate the concepts of accuracy and precision in a practical fashion, let us consider evaluating the results of the four shooting sessions of Figure 2 that the sharp dart shooter completed at different times. In session A, the shooter’s shots seem to be scattered around the bullseye. He/she managed to get the shots around the targeted spot, or the bullseye, but failed to land them close to each other, i.e. they are scattered apart. To evaluate such a session, we say the shooter was accurate as he/she stayed close to the bullseye, but not precise, as the shots were not close to each other. In session B, we would say the shooter managed to cluster all shots in one spot, so he/she was precise but far away from the bullseye, so he/she was not accurate. Accordingly, in session C, he/she was accurate and precise, while in session D the shooter was neither accurate nor precise. To illustrate the concept of biases in measurements, let us analyze sessions B and C. Assuming the two sessions were shot by the same shooter, it is obvious that the shooter performed perfect shots in both sessions but that his/her shots in session B were biased due to mechanical misalignment of the bow or the gun, if a gun was used. Such misalignment of the bow, the gun barrel, or the sight scope caused the shots to be systematically directed to the wrong position instead of the bullseye, causing a bias in the shots. Once proper calibration is made to these mechanical defects, the bias is then removed and all the shots will perfectly fall around the bullseye, like in session C.
To evaluate the shooter results using probability and density distribution terms, the results of session B are equivalent to the random distribution 3 of Figure 3, precise but not accurate, assuming the most probable value of the bullseye is represented by p on the x-axis. The results of session A, however, resemble the distribution 2 of Figure 3, accurate but not precise. For more information on the subject, please watch this NGS video.