Optimal Sample Size Selection for Continuous In-Process Data using a Quality Assessment

Abstract

In the statistical test, the evidence which can permit the rejection of the null hypothesis and make a conclusion that the program has an effect is done. There is always a difference in any groups which takes part in the statistical tests. The power of the study normally refers to the probability that the researcher will find the difference which exists between the groups taking part in the statistical tests especially when it exists. It simply means that it is the ability to fail to accept the null hypothesis when it is required so. The performance of the size estimation and the power analysis is very significant in the experimental design since when there is no these computation, the size of the sample may be too low or high. In the case where the sample size is too low, the experiment process will not provide the valid and reliable results to the investigated questions. In the situation where the sample size is too large, the wastage of time and other resources will be manifest with a very smaller gain. Therefore, the main purpose for the size estimation and power analysis is to provide the scientific methods which can be used to give an answer to the questions accurately, quickly and very easily. Key Terms – Continuous In-process Data, Discrete Data, Process Sampling, Sample Power Analysis, Sample Size.