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Accuracy vs Precision

Scientists like to be both accurate and precise when conducting their experiments. We can never make a perfect measurement. The best we can = do is to come as close as possible within the limitations of the measuring instruments.

Here is a model to demonstrate the difference.=

Suppose you are aiming at a target, trying to hit the bul= l's eye (the center of the target) with each of five darts. Here are some representative pattern of darts in the target.

Neith= er Precise Nor Accurate
	This is a randomlike pattern, neither precise nor accur= ate. The darts are not clustered together and are not near the bull's eye.

Precise, Not Accurate
<= !--[if gte vml 1]>	This is a precise pattern, but not accurate. The darts are clustered together but did not hit the intended m= ark.

Accurate, Not Precise
<= !--[if gte vml 1]>	This is an accurate pattern, but not precise. The darts are not clustered, but their 'average' position is the center of the bull's eye. =

Precise and Accurate
<= !--[if gte vml 1]>	This pattern is both precise and accurate. The darts are tightly clustered and their average position is the center of the bull's eye.

Accuracy: You can be accurate every time you do an experiment even though there might be multiple errors in the way the experi= ment was conducted. Thus, for the inaccurate method, your result is accurate, bu= t it is not precise.

Precision: When scientists perform an experiment mult= iple times, with no errors in its method, and they receive similar results, they= are precise. In other words, although they didn't hit the bull's eye (i.e. they weren't accurate), they were somewhat close to the actual result and thus precise.

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