See the related link for the area at 0.41 (same as -0.41) which is 0.1591. This area, which is the probability, is from minus infinity to -0.41. If you want the area from -0.41 to plus infinity you need to take 1 - 0.1591 which is 0.8409.
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In the field of analytical measurement, the z-multiplier is a measure of error. It indicates a statistical probability of error. It is calculated using standard formulas for normal distribution.
You may transform a normal distribution curve, with, f(x), distributed normally, with mean mu, and standard deviation s, into a standard normal distribution f(z), with mu=0 and s=1, using this transform: z = (x- mu)/s
Yes, except that if you know that the distribution is uniform there is little point in using the empirical rule.
A test using relative errors comparing a frequency table to the expected counts determined using a given probability distribution; the null hypothesis is that the given probability distribution fits the data's distribution.
It is necessary to use a continuity correction when using a normal distribution to approximate a binomial distribution because the normal distribution contains real observations, while the binomial distribution contains integer observations.