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With this sampling scheme, the expected sample size will be around 275,000 women.

Design parameters are chosen to minimize expected sample size subject to frequentist error constraints.

We observe that the proposed sampling design minimizes the expected sample sizes in different situations.

The expected sample will be 10 students who will be assessed using the revised EPQ-RS and the TAI.

Through simulation studies, we evaluate the sensitivity of Type I error, power, expected sample size, and trial duration to different design characteristics.

In addition, the study parameters were determined by comparing the assessment indexes such as total sample size, expected sample size and the test power at the first stage.

The profiles of p-specific expected sample size, mean squared error and risk under different approaches motivate us to introduce the concept of "admissible sample size (design)".

If the effect size is smaller than expected, sample size recalculation would be a reasonable choice in the face of an underpowered study.

The conventional optimal design, for given type 1 and type 2 error rates, is the one which minimises the expected sample size under the null hypothesis.

We dropped this assumption and computed the expected power and expected sample size to evaluate the performance of the procedure under the omniscient model.

As expected, sample with the highest surface area, C3060, possesses the highest gravimetric capacitance at all examined discharge current densities (0.2 2 A/g).

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