Introduction

In the context of statistics, accuracy describes how closely a measured value agrees with the true value of that quantity. Three key ideas are central to this definition: (1) an observed value does not necessarily reflect the true value; (2) there are errors in measurement and calculation; and (3) we may want to measure or calculate values precisely to reduce these errors.

Accuracy is a description of random errors

Accuracy is a measure of how closely an observed or calculated value agrees with the true value. It is important to distinguish between accuracy and precision because there are two kinds of errors:

  • Systematic errors, which arise from using incorrect or missing data and can be resolved by recalculating the results;
  • Random errors (sampling error), are inherent in any calculation and show up as statistical variability around a mean.

Accuracy refers to random errors; it’s concerned with how closely observations (or calculated values) agree with true values. Precision refers to systematic errors; it describes how close repeated calculations will be — assuming they’re done properly every time.

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It is possible to make sense of accuracy without any extensive mathematical intervention

A good definition of accuracy is that it is a measure of how closely the observed value corresponds to the true value. Accuracy is a statistical concept, but it’s not the same thing as precision. You can think of accuracy as being just another way to describe precision. 

For example: if you measure someone’s height with an instrument that measures in tenths of inches and gets 9’7″, then your measurement was very precise (you measured exactly where he was standing), but not very accurate because 9’7″ isn’t really what his height was (it could be 9’6″ or even 10′). The most accurate measurement would have been something like “almost exactly nine feet tall.”

Some people use different words for accuracy than I do here—they might say precision instead—but they’re talking about something similar: how close our measurements are to reality. So when someone says they want more “precision,” they might be asking for more “accuracy.” This distinction isn’t always clear even among scientists who study measurement techniques specifically because there aren’t agreed-upon definitions yet.*

The definition of accuracy reveals a set of essential

The definition of accuracy reveals a set of essential ideas that are closely connected to the accuracy concept. A measurement is accurate if it corresponds to its actual value. The actual value may be unknown or unknowable, but for any given measurement, the true value can always be determined. Accuracy does not mean perfection: an accurate measure is only as good as its instrumentation (or lack thereof).

The word “accurate” comes from the Latin roots ad-, meaning “to,” and corrigere, meaning “to correct.” When we’re trying to measure something with high degrees of precision and consistency, we want our instruments to be able to make corrections so that they’re getting closer and closer over time to measuring what they’re designed for accurately; this process involves making adjustments based on some kind of comparison with another set of measurements taken under similar conditions by different people using different methods to achieve mathematical consensus between these various approaches (which is why statistics play such an important role when working with data).

A definition of accuracy can be given in a single sentence

Accuracy is the closeness of agreement between a measurement and a true value. It can be given in a single sentence: Accuracy is a measure of how closely observed or calculated values agree with true values.

Accuracy describes how closely the measured value of a quantity corresponds to its actual (true) value

Accuracy is often used synonymously with precision, though sometimes a distinction is made between them: “precision means that the observations are repeatable and accurate means that the measurements agree with each other.”[2] For example, if you measure someone’s weight to be 150 lbs, and then measure it again in 10 minutes to find out that it has changed by 1 lb over those 10 minutes, you would not consider this person’s weight to be very precise (since their weight can vary by more than 1 lb as time passes), but you might still consider their measurement accurate since they have been consistent throughout these measurements.[3]

Statistical accuracy is related but different: statistical accuracy can refer to either a single measurement or an entire set of measures; statistical accuracy involves determining whether groups of observations differ from one another based on random chance rather than systematic error or bias.[4][5][6]

Accuracy is a measure

Accuracy is the closeness of agreement between a measured value and the true value. It’s a measure of how closely an observed or calculated value agrees with the true value.

Accuracy is a property of a measurement procedure, not of its result. A measurement is accurate only if performed correctly, by the requirements specified by its governing standard (such as ISO 27005).

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Conclusion

The concept of accuracy is a very important one in science and engineering because it helps us to understand how closely our measurements and calculations agree with their true values. Our definition tells us that the more accurate a measurement or calculation is, the closer its observed value will be to its true value.

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