The Statistical Significance

Statistical Significance
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Statistical Significance
Statistical hypothesis testing is a confirmatory data analysis method that involves comparing two sets of statistical data. It is a decision making technique that uses data from controlled and or uncontrolled experiment. The two statistical data sets obtained through sampling are compared with data from an idealized model and a hypothesis proposed in efforts to identify the statistical relationship if at all that exists. The next step in the hypothesis testing is the determination whether the difference in the sampled data are statistically significant or not. Donald and Pamela note in their book that “a result is statistically significant if it is unlikely to have occurred by chance alone or if there is reason to believe that the difference does not represent random sampling fluctuations only” (Cooper & Schindler, 2014).
The random sample conducted at the university to determine the political affiliations of the professors and students observed varying responses. The research was carried out on a sample of 20 professors and 20 students on their political stands regarding the conservative party. It was then obtained that 10 professors and 6 students of the random sample responded to affiliate to the conservative party. In addition to the survey and the responses obtained the burden that ensues is to figure out and prove if the difference in the result is statistically significant. A statistical test as with the aforementioned case will incorporate two hypotheses in its analysis and comparability efforts. Firstly being the null hypothesis that will consider that none of the professors and students sampled are conservatives (Baker & Mudge, 2012). Secondly is the alternative hypothesis that will examine the professors and students that are conservative. The two hypotheses as Lempert notes in the article ...