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In statistical hypothesis testing, a type I error, or a false positive, is the rejection of the null hypothesis when it is actually true. A type II error, or a false negative, is the failure to reject a null hypothesis that is actually false. [1] Type I error: an innocent person may be convicted. Type II error: a guilty person may be not convicted.
The specificity of the test is equal to 1 minus the false positive rate. In statistical hypothesis testing, this fraction is given the Greek letter α, and 1 − α is defined as the specificity of the test. Increasing the specificity of the test lowers the probability of type I errors, but may raise the probability of type II errors (false ...
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For a Type I error, it is shown as α (alpha) and is known as the size of the test and is 1 minus the specificity of the test. This quantity is sometimes referred to as the confidence of the test, or the level of significance (LOS) of the test. For a Type II error, it is shown as β (beta) and is 1 minus the power or 1 minus the sensitivity of ...
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As a coincidence, 1 NM is about 6,000 feet (6,076.1 feet) so we can use the 60:1 rule for this too. For a 1 degree shift at 1 NM, there are about 100 feet of offset. This becomes very useful for estimating or correcting vertical speed settings and flight path angles (FPA) during climb, descent, or approaches.
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The Basic Status Codes have been in SMTP from the beginning, with RFC 821 in 1982, but were extended rather extensively, and haphazardly so that by 2003 RFC 3463 rather grumpily noted that: "SMTP suffers some scars from history, most notably the unfortunate damage to the reply code extension mechanism by uncontrolled use.