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When we make any measurement, we can never be certain what the result exactly is. There is always an uncertainty associated with the measurement. If the uncertainty is small compared to the intended purpose, then we can confidently use our measurements to make good decisions. If the uncertainty is large, then we may end up making poor decisions. Hence, it is important to quantify the uncertainty when measurement values are being reported so that users can have a good idea how much to trust the measurement results. Often times in literature, measurements are reported without accompanying uncertainty numbers. For example, the speed of the car is 60 kmph, the length of a critical dimension is 2.54 mm etc. If we had incorporated uncertainty into our measurements, then we would report for example, the speed of the car is 60 ± 5 kmph, or the length is 2.54 ± 0.01 mm. In this article, we will talk about what causes uncertainty and how to calculate and report uncertainty along with our measurements.
Any measurement that you make without the knowledge of its uncertainty is completely meaningless - Prof. Walter Lewin
|Type A||s/√n||Take n repeated measurements and calculate the mean and standard deviation (s)|
|Type B||R/√12||If we know the range of measurement values (R) and if the distribution is uniform. If the distribution was normal, then we would use a different constant to obtain the standard deviation.|
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