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. 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

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

Examples of Type I error in a sentence

• Finally, researchers should be aware of the impact of multiple comparisons on Type I error.

• Post- hoc pairwise comparisons with Type I error controlled across the tests using the Bonferroni approach showed that students in the scaffolded only condition performed significantly better than students in the control condition (Cohen’s d = 0.45), but there was no significant difference between the two scaffolded conditions.

• Finally, researchers should be aware of the impact of multiple comparisons on Type I error because performing numerous statistical significance tests of trends increases the likelihood of inappropriately concluding a change is statistically significant.

• Type I error enhancement due to multiple significance testing will be accounted for if applicable.GCP conformance:The present trial will be conducted in accordance with the valid versions of the trial protocol and the internationally recognised Good Clinical Practice Guidelines (ICH-GCP), including archiving of essential documents.Participating centres:University Hospital MünsterFinancing:Financing for the intervention study will be applied for to the Else- Kröner-Fresenius-Stiftung.

• Based on the simulation study, Chen (1995b) suggests using either the z-value or average of the z-value and t-value when n (the sample size) is small (e.g., n ≤ 10) or α (the Type I error) is small (e.g. α ≤ 0.01), and using either the t-value or the average of the z-value and t-value when n ≥ 20 or α ≥ 0.05.The function chenTTest returns three different p-values: one based on the normal distribution, one based on Student’s t-distribution, and one based on the average of these two p-values.

• In this case, assuming normally distributed data, you perform a one-way parametric analysis of variance.In the course of designing a sampling program, an environmental scientist may wish to determine the relationship between sample size, Type I error level, power, and differences in means if one of the objectives of the sampling program is to determine whether a particular mean differs from a group of means.

• For the test against the upper alternative in (2) above, this leads to a Type I error smaller than the one assumed and a loss of power (Chen, 1995b, p.767).Similarly, in the case when the underlying distribution of the n observations is negatively skewed and the sample size is small, the sampling distribution of the t-statistic is positively skewed.

• The fundamental issue in quality control centers on the tradeoff between falsely rejecting good data (Type I error) and falsely accepting bad data (Type II error).

• Correction for multiple comparisons (Type I error) was conducted using the Benjamini and Hochberg false discovery rate (FDR) controlling procedures [1], with Q value set as 0.1. The effects of 48/80 and GTN on MC responses was assessed using unpaired student’s t-test.

• This way, 45 jobs, 16 qualifications and 10 restricted occupations were identified within the NPP decommissioning phase.Usually a qualification covers several jobs.