That is, one decides how often one accepts an error of the first kind a false positive, or Type I error.
32.
When the " p "-value is calculated correctly, this test guarantees that the Type I error rate is at most " ? ".
33.
Furthermore, it is also claimed that if the underlying assumption of homoscedasticity is violated, the Type I error properties degenerate much more severely.
34.
Choosing rules once the data have been seen tends to increase the Type I error rate owing to testing effects suggested by the data.
35.
The Wikipedia article on type I error, as well as numerous Google search results, refers to type I errors as " false positives ".
36.
For a fixed level of Type I error rate, use of these statistics minimizes Type II error rates ( equivalent to maximizing power ).
37.
The Wikipedia article on type I error, as well as numerous Google search results, refers to type I errors as " false positives ".
38.
How did " false positive " come to be equated with type I error ?-- 64.236.170.228 19 : 32, 6 September 2007 ( UTC)
39.
Each of these corrections have been developed to alter the degrees of freedom and produce an F-ratio where the Type I error rate is reduced.
40.
The above pattern recognition example contained 7 & minus; 4 = 3 type I errors and 9 & minus; 4 = 5 type II errors.
