# What Is The Difference Between Accuracy And Precision

When discussing the issue of measuring something, the focus shifts from the object to the accuracy or precision of the measurement. In daily life, accuracy and precision are frequently used synonymously. They are, however, defined differently in terms of measurement.

A measurement is not necessarily precise just because it is accurate, and vice versa. Producing work of high caliber requires both accuracy and precision, but what are they? Well, read on as we uncover what you need to know about accuracy and precision.

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## Accuracy & Precision Definition

Scientists think of error in two ways: precision and accuracy. Accuracy refers to how close a measurement is to the true or acceptable value. Precision, on the other hand, refers to how closely two measurements of the same thing are. Accuracy has no bearing on precision. This implies that one might be accurate without being precise, as well as extremely precise without being accurate. The best-quality scientific observations are both accurate and precise.

## Difference Between Accuracy And Precision

Simply put, precision refers to how consistently and dependably the experiment’s measurement is made. On the other hand, accuracy verifies how closely the experiment measurement approaches the ideal or acceptable value. If a basketball player were to attempt a shot at the hoop, the ball would either fall into the basket or very close to it. This is a perfect analogy to help you remember accuracy and precision.

The ball would land precisely where it would, whether it was near or far from the hoop, if the player attempted to shoot with precision. A good player would consistently shoot the ball into the basket while being accurate and precise. For instance, your measurement is inaccurate if you weigh a substance in the lab and find that it weighs 3.2 kg, while the real weight is 10 kg.

Your measurement in this instance is far from the known value. In the aforementioned example, your measurement is extremely precise if you weigh a certain substance five times and obtain 3.2 kg each time. Accuracy has no bearing on precision. This brings us to the conclusion that you might be extremely precise yet inaccurate, and you might also be accurate but not precise.

## A Perfect Example

Using a dartboard is a traditional method of illustrating the difference between accuracy and precision. Consider the center of a dartboard as the genuine or correct value. The accuracy of darts increases with their distance from the bullseye.

• There is neither accuracy nor precision (on dartboard A) if the darts are not in close proximity to the bullseye or to one another.
• There is precision with no accuracy (on dartboard B) if every dart lands extremely close to one another but far from the bullseye.
• There is mathematical accuracy if all of the darts are positioned equally around and away from the bullseye, with the average dart falling within the target (on dartboard C). This is a representation of rather precise yet accurate data. But in a real game of darts, this would not be considered a bullseye!
• There is accuracy as well as precision (on dartboard D) if the darts land near the bullseye and near each other.

## How To Track And Measure Accuracy And Precision

You must track and measure your performance in order to determine your accuracy and precision. Precision and accuracy are measured in many ways:

• Measuring accuracy involves comparing your outcome to the desired value. You are more accurate the closer you are. You must have well-defined objectives or success criteria in mind in order to calculate accuracy.
• How closely measurements match one another is known as precision measurement. In order to assess precision, you must create a tracking system that illustrates the evolution of relationships between various outcomes or data points.

## Conclusion

In conclusion, it is critical to differentiate between accuracy and precision when working in science. Precision relates to the consistency of outcomes, whereas accuracy entails being close to the correct value. This distinction is the foundation of the dependability of scientific measurements and analyses.