Type I error definition

Type I error means the error made when it is concluded that an area of a site is below cleanup levels when it actually exceeds cleanup levels. This is the rejection of a true null hypothesis.

Type I error means in a statistical test, incorrectly indicating pollution or an increase in pollution;

Type I error. Deciding favoritism exists when there is, in fact, no favoritism. TYPE II ERROR: Deciding parity exists when there is, in fact, favoritism. The probabilities of each type of each are: TYPE I ERROR: [OBJECT OMITTED]. TYPE II ERROR: [OBJECT OMITTED]. We want a balancing critical value, cB, so that (alpha) = (beta). It can be shown that. [OBJECT OMITTED]. where [OBJECT OMITTED] [OBJECT OMITTED] (PHI)() is the cumulative standard normal distribution function, and (phi)() is the standard normal density function. This formula assumes that Zj is approximately normally distributed within cell j. When the cell sample sizes, n1j and n2j, are small this may not be true. It is possible to determine the cell mean and variance under the null hypothesis when the cell sample sizes are small. It is much more difficult to determine these values under the alternative hypothesis. Since the cell weight, Wj will also be small (see calculate weights section above) for a cell with small volume, the cell mean and variance will not contribute much to the weighted sum. Therefore, the above formula provides a reasonable approximation to the balancing critical value. The values of mj and sej will depend on the type of performance measure. Mean Measure For mean measures, one is concerned with two parameters in each cell, namely, the mean and variance. A possible lack of parity may be due to a difference in cell means, and/or a difference in cell variances. One possible set of hypotheses that capture this notion, and take into account the assumption that transaction are identically distributed within cells is: H0: (mu)1j = (mu)2j, (sigma)1j2 = (sigma)2j2

Examples of Type I error in a sentence

• Type I error occur when an authority prohibits procompetitive mergers, whereas type II errors imply outright clearance of anticompetitive mergers that should have been prohibited or challenged by remedies.

• The average Type II error rate when using a 0.10 critical alpha in this case is five times the Type I error rate, and the median Type II error rate is over six times the Type I rate.

• Power for tests of interaction: effect of raising the Type I error rate.

• Information that indicates an increased Type I error likelihood will help target alpha level adjustments to decrease Type I error where it is likely to be more beneficial.

• Alpha level adjustments are helpful to decrease Type I error especially for large samples.

• Holding the single-month alpha level constant for identifications requiring consecutive monthly failures produces a much lower net Type I error rate than the rate for the single-month assessment.

• Even if the increased Type I error rate of 0.20 was applied to all parity tests, the average Type II error rate would still be twice as large even when we limit detection to performance two times worse to CLEC versus ILECcustomers.

• The code that originally did not match will now match the data, creating a false positive or Type I error.

• As the excess proportion of high SG firm-quarters increases, Type I error rates for the Jones and the Mod-Jones(C) models, with and without adjustment for ROA, increase rather dramatically.

• We see that both meth- ods control the Type I error, unlike bootstrapping from the residuals, and that both methods are conservative.