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

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

  • Finally, researchers should be aware of the impact of multiple comparisons on Type I error (i.e., the chance of declaring an observed difference to be statistically significant when there is no difference in the population parameters).

  • Performing numerous statistical significance tests increases the likelihood of a Type I error.

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

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

  • 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.

  • The final analysis for the primary objective in the PP-EFF population will be carried out at the overall one-sided Type I error rate of 0.025 for the 3 planned analyses.

  • The interim and final analyses for the primary objective in the PP-EFF population will be carried out at the overall one-sided Type I error rate of 0.025.

  • Also, ▇▇▇▇▇-▇▇▇▇▇ simulations can be used to derive empirical criteria that maximize the ability to identify both uniform and nonuniform DIF, and control for the overall Type I error rate (▇▇▇▇, ▇▇▇▇▇▇▇, & ▇▇▇▇▇, 2011; ▇▇▇▇▇▇, Flens, de Beurs, ▇▇▇▇▇▇, & ▇▇▇▇▇▇, 2022).

  • Of course, 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.