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Statistical or Random Errors Every measurement an experimenter makes is uncertain to some degree. The uncertainties are of two kinds: (1)random errors, or (2) systematic errors. For example, in measuring the time required for a weight to fall to thefloor, a random error will occur when an experimenter attempts to push a button that starts a timersimultaneously with the release of the weight. If this random error dominates the fall time measurement, then ifwe repeat the measurement many times (Ntimes) and plot equal intervals (bins) of the fall time tion the horizontal axis against the number of times a given fall time tioccurs on the vertical axis, our results (see histogram below) should approach an ideal bell-shaped curve (called a Gaussian distribution) as the number ofmeasurements Nbecomes very large. The best estimate of the truefall time tis the meanvalue (or average value) of the distribution ⟨t⟩: If the experimenter squares each deviation from the mean, averages the squares, and takes the square root of that average, the result is a quantity called the "root-mean-square" or the "standard deviation" sof the distribution. It measures the random error or the statistical uncertainty of the individual measurement ti: About two-thirds of all the measurements have a deviation less than one sfrom the mean and 95% of all measurements are within two sof the mean. In accord with our intuition that the uncertainty of the meanshould be smaller than the uncertainty of any single measurement, measurement theory shows that in the case of random errors the standard deviation of the mean, sm, is given by: PHY133 - Classical Physics I Laboratory Error Analysis ⟨t⟩= (ΣNi=1ti)/N. (1)s= √[ΣNi=1(ti- ⟨t⟩)2/ (N-1) ]. (2)Page 1 of 59/19/2003PHY117/8 - Error Analysis
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