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One-Tailed Test or a Two-Tailed Test - Assignment Example

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In the paper “One-Tailed Test or a Two-Tailed Test” the author contrasts and compares a t-test for two samples, assuming equal variances, with an alpha value of 0.05. The first step in any t-test analysis is setting both the null and alternative hypotheses…
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One-Tailed Test or a Two-Tailed Test
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Download file to see previous pages If there were foreknowledge that there was no expected difference, it would be absurd to collect the data and do the analysis. In this case scenario, the comparison is between two groups of people in which one has received oxygenation medication whereas the other one has not.  Jackson (2011) asserts that the use of one tailed analysis is predicated upon a high certainty prior to the data collection that either there is no difference or a difference exists in a certain area of the entire population. In the event that the data analysis ends in showing the existence of a difference in the incorrect region, it then becomes automatic that the difference is attributable to random sampling. This consideration or assumption is done without giving due thought to the possibility that true difference might be a reflection of the measured or calculated difference (Urdan, 2005). The Probability That Group I Is Different from Group 2 and the Significance Against the Benchmark of P < .05  According to Rasch, Kubinger, and Moder (2011), the null hypothesis can only be rejected when the t-static from tables is less or greater than t critical two-tail value gotten after computation. Therefore, if the test statistic is less than -2.12 or greater than 2.12, the null hypothesis will be rejected and the alternative hypothesis adopted instead.  The test statistic is 0.899, which falls into the rejection region, so the null hypothesis is not rejected, which states that there is no difference between the means from the two samples.  ...Download file to see next pagesRead More
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