Six Sigma Tools for Testing Statistical Significance

Six Sigma Tools for Testing Statistical Significance
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Hypothesis testing is a statistical method that is used to determine whether a hypothesis regarding a certain perimeter in a given amount of data usually referred to as a population, using data measured in a sample is true CITATION Pec11 \l 1033 (Peck, Olsen, & Devore, 2011). The test aims at determining the likelihood of a certain statistical sample could be selected and the hypothesis tested is true. It is also known as significance testing and it is a process used to select samples and get more information of its characteristics in a sample CITATION Pec11 \l 1033 (Peck, Olsen, & Devore, 2011).
Hypothesis testing involves 4 steps that are; identification of a hypothesis to be tested, determining rationale by which the hypothesis will be determined to be true or not, select a random sample and get the mean, and compare the sample observation to what we expect as true or not CITATION Pec11 \l 1033 (Peck, Olsen, & Devore, 2011).
Several methods are applied in hypothesis testing that depends on the number of variables used and the type of statistic being tested the common ones being the t- statistic and the Z- statistic CITATION Sah13 \l 1033 (Sahoo, 2013). However, this paper will focus on the t-statistic. The t-statistic was developed by Gosset and used to predict if two sets of data following a normal curve are similar or different. CITATION Sah13 \l 1033 (Sahoo, 2013) There two approaches in the t-statistic, that is for the unpaired (independent) samples where separate sets of data that are identical in distribution and independent occur. The other is for the unp...