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Accuracy Vs Precision


    Precision is the degree to which several measurements provide answers very close to each other. It is an indicator of the scatter in the data.The lesser the scatter, higher the precision. The picture clearly describe Accuracy.


      Accuracy describes the nearness of a measurement to the standard or true value, i.e., a highly accurate measuring device will provide measurements very close to the standard, true or known values.Example: in target shooting a high score indicates the nearness to the bull's eye and is a measure of the shooter's accuracy 

Precise Vs Accurate 

preciseaccurateprecise and accurate


It is important to understand that measurements (observations) may be precise but not accurate if they are closely grouped together around a value that is different from the expectation ("true" value) by a certain amount. Also, observations may be accurate but not precise if they are well distributed about the expected value but are significantly dispersed from one another. Finally, observations can be both precise and accurate if they are closely grouped around the expected value (the distribution mean). 

Is it better to be accurate then Precise?

This question is at the heart of any experimental scientist.  Any scientist will tell you it better to be both but that kind of a cop out.  Here is the break down of precision and accuracy.  

To run an experiment and to get experimental value that is accurate but not precise, then you have a great deal of random error but have little systematic error. 

To run an experiment and to get experimental value that is precise but not accurate, then you have a great deal of systematic error but have little random error. 

     So in my opinion i think to be precise and not accurate is better the the other way around.  To have poor precision means that you didn't plan well enough, but to have poor accuracy means that you lack lab skills. This isn't a hard and fast rule because when performing some experiments it may be hard to avoid random or systematic error.