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Answer :
Reproducible data that are not accurate consistently yield the same results, but those results do not match the true value. Accuracy and precision are both required for valid results, and data must be both to be reliable for scientific purposes.
When data are reproducible but not accurate, this means that while the results can be consistently achieved under the same conditions, they do not closely match the true or intended value. Accuracy is the measure of how close a calculated or measured quantity is to its actual value. In contrast, precision, also known as reproducibility or repeatability, refers to the consistency of repeated measurements or results. Thus, data that are reproducible but not accurate are precise in that they yield the same results consistently, but those results are systematically deviant from the true value.
For example, if a clock consistently measures time with a difference of five minutes compared to the actual time, it is precise because it always shows the same time deviation but it is not accurate. To draw an analogy, imagine a dartboard where all darts land in a tight cluster, but this cluster is not at the center of the target; this represents reproducibility without accuracy. Ensuring that data are both accurate and precise is paramount, as only then can they be considered valid. Without accuracy, the reproducibility of data does not guarantee its reliability for predicting or reflecting the actual characteristics of the phenomenon being measured.
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Rewritten by : Batagu
The data can be produced over and over but are not close to the accepted value. hope that helps!
