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As a tutor in a local level college, am always faced with projecting results for the computer class to the parent's meeting. These projections are based on the average marks for the whole class in which those parents get the insights of their students’ performance before, the results are out. Therefore, the null and alternative hypotheses may be stated as; H0: The students’ mean mark for the computer class will be 50% for this semester.H1: The students’ student’s mean mark for the computer class will be less than 50% this semester.
From these two propositions, the first one (H0) is the null hypothesis while the H1 is the alternative hypothesis. Such an idea is based on the idea of probability and the rejecting of the hypothesis may be influenced by different factors (John, 2007). If the students do not revise well and cover the expected chapters, their performance will be adversely affected and the chances are high that they will not attain the mean mark of 50%. Therefore, under such circumstances, the null hypothesis will be rejected and conclude that the mean mark was less than 50%.
However, Type I error may oca cur whereby the H0 is rejected while the students had performed to the average mark. Such an idea may be caused by computational errors or using a poor approach (John, 2007). On the other hand, we could accept the H0 and conclude that the mean mark was on average 50% when in reality the mean mark was less than 50%. Such an aspect allows the tutor to report a false result (John, 2007).
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