Is it true that good accuracy of data ALWAYS indicates good precision?
No. Consider.
Actual value = 20.00%
Values reported:
20.00%
30.00%
10.00%
Avg = (20.00+30.00+10.00)/3 = 20.00
Great accuracy.
Lousy precision. In fact with precision like this no one would even report the values; most likely the work will be repeated.
No, it is not always true that good accuracy of data indicates good precision. Accuracy and precision are two different concepts in the context of data measurement.
Accuracy refers to how close the measured value is to the actual value or true value. It measures the absence of systematic errors or bias in the data. If the measured values are consistently close to the actual values, then the data can be considered accurate.
Precision, on the other hand, refers to the level of consistency or reproducibility of the measured values. It measures the absence of random errors or variability in the data. If the measured values are consistently close to each other, then the data can be considered precise.
It is possible to have good accuracy but poor precision, or vice versa. For example, if a measurement is consistently biased high or low (systematic error), then it would be inaccurate but potentially precise if the measured values are close to each other (even if they are not close to the true value). Conversely, if the measured values are spread out or scattered (random error), then they would be imprecise even if the average value is close to the true value (and therefore accurate).
In summary, while good accuracy can indicate good precision, it is not always the case. It is important to consider both accuracy and precision separately to assess the quality of data.
No, it is not always true that good accuracy of data indicates good precision. Accuracy and precision are two distinct concepts in the field of measurement and data analysis.
Accuracy refers to how close a measured value is to the true or target value. It indicates the absence of a systematic error in the measurements. In other words, accuracy tells us whether the measurements are on target.
Precision, on the other hand, refers to the consistency or reproducibility of measurements. It indicates how close multiple measurements are to each other, regardless of whether they are close to the true value. Precision tells us whether the measurements are consistent and reliable.
While accuracy and precision are related, they are not synonymous. It is possible to have good accuracy but poor precision, and vice versa. Let's look at two examples to understand this better:
1. Example of good accuracy and precision:
If a target value is 10, and you take multiple measurements that are all around 9.8, 9.9, and 10.1, then you have good accuracy (the measured values are close to the true value) and good precision (the measurements are close to each other).
2. Example of good accuracy but poor precision:
If a target value is 10, and you take multiple measurements that are spread out, for example, 9.5, 9.8, 10.3, and 10.6, then you have good accuracy (the average of the measurements is close to the true value) but poor precision (the measurements vary significantly from each other).
In summary, accuracy and precision are separate considerations when evaluating the quality of data. Good accuracy does not always indicate good precision, and vice versa.