Using complete sentences, I will explain how a set of experimental data can be accurate but not precise, precise but not accurate, and neither accurate nor precise.
a. If a set of experimental data is accurate but not precise, it means that the data is close to the true value or target, but the measurements or values are not consistent or repeatable. In other words, the data points may be scattered or vary widely from each other, but their average or mean value is close to the true value. This can happen due to random errors or uncertainties in the measurement process.
b. On the other hand, if a set of experimental data is precise but not accurate, it means that the measurements or values are consistent or repeatable, but they are not close to the true value or target. In this case, the data points may cluster tightly around a single value, but that value may be different from the expected or true value. This can happen due to systematic errors or biases in the measurement process.
c. Finally, if a set of experimental data is neither accurate nor precise, it means that the measurements or values are neither close to the true value nor consistent or repeatable. The data points may be scattered or vary widely from each other, and their average or mean value may not be close to the true value. This can happen due to a combination of random errors and systematic errors in the measurement process.
In summary, accuracy refers to how close the measured values are to the true value or target, while precision refers to the consistency or repeatability of the measurements. A set of experimental data can be accurate but not precise, precise but not accurate, or neither accurate nor precise, depending on the combination of these factors.
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