Have Confidence in Your Statistical Analysis!: Learning How to Use Confidence Intervals

Its nice article. I think the example 3 is reversed in mentioning Type I & Type II risk.
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Agreed this is a good article. A point estimate may though not be another useless number. If the standard deviation is already well known, such as a test for which a comprehensive GRR has been performed (where repeats >>10) or a control is being run on a frequent basis, then the std dev from these can be used. The std dev from these can be much better since it's based on far more repeats. A finite interval is given even when the number of repeats is 1. Use the Z in that case. A greater amount of repeat measurements is needed when one wants a tighter range of 95% conf limits.
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Hi rschweiger, Initially I also thought the same way as you did. Your statement (rejecting good part is type I risk) also correct. If we see the type1 risk in confidence interval perspective, wrongly estimating the population mean is type 1 risk - ie., our calculated confidence interval do not have population mean. Example if we calculate CI 100 times by taking samples from population, we may be wrong 5 times for 95% Confidence interval, but we do not know when this 5 times going wrong is not known. In this example type 1 error is 5%. Type 1 error for CI is risk based on result of estimation. Type 1 error in sampling inspection is risk based on result of action taken.
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thanks for sharing the article.
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Excellent article. Do you have your Type I and Type II risks reversed in examples 3 and 4? Usually, rejecting something when it is actually good is the Type I risk. Or am I being overly simplistic and/or missisng something?
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