Difference between data accuracy and data precision?

Question: Difference between data accuracy and data precision?

Data accuracy and data precision are two important concepts in data analysis that help to determine the quality of the data being analyzed.

Data accuracy refers to how close the measured or observed values are to the true values. In other words, data accuracy measures how correct or true the data is. For example, if a thermometer measures the temperature of a room to be 70 degrees Fahrenheit, and the actual temperature is 70 degrees Fahrenheit, then the data is said to be accurate.

Data precision, on the other hand, refers to how consistent the measured or observed values are with each other. In other words, data precision measures how exact or reproducible the data is. For example, if a scale consistently measures the weight of an object to be 100 grams, then the data is said to be precise.

To summarize, accuracy measures how close the data is to the true value, while precision measures how consistent the data is. A dataset can be accurate but not precise, precise but not accurate, both accurate and precise, or neither accurate nor precise.

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