The one-sample hypothesis test is a procedure in statistics that determines whether observations made may have been as a result of a process with a particular mean (Lumenlearning, n.d). For instance, an individual is interested to see whether the line of assembly produces laptops weighing five pounds. This is a hypothesis and to test this, he could sample some of the laptops in the line of assembly, weigh them and come up with a comparison between the sample and the value of five using one-sample hypothesis testing.
This testing is inclusive of both alternative and the null hypothesis. The alternative hypothesis follows the assumption that the value of comparison and the true mean have some differences. On the other hand, the null hypothesis follows that both values are indifferent (Lumenlearning, n.d). The purpose of this type of hypothesis testing is to determine if the null hypothesis holds no ground and be rejected a given a data sample. As this is a procedure dealing mostly with the estimation of parameters that are unknown, there are several assumptions made (Corder & Foreman, 2014). Evaluating how one’s results deviate from the assumptions is one way to assess if they are quality. The assumptions include:
Observations are not dependent on each other.
The dependent variable ought not to have any outliers, should be continuous (ratio/interval) and also must be nearly distributed normally.
Data in the real world is rarely normal, thus to satisfy this assumption, a nearly symmetric bell-shape has to be identified on a histogram.
This hypothesis test bases a result on a level of significance, meaning it was highly unlikely to occur by chance alone (Corder & Foreman, 2014). Notably, one sample testing does not depend on data but on the process. Therefore, with the example of the aforementioned laptops, the real question was aimed at whether the process producing the laptops had a mean weight of five. This test has also been called the confirmatory test which is a key approach of frequency inference. They are fixers for the likelihood of deciding incorrectly that the default state (null hypothesis) is not correct basing it on a sample of observations to happen if the null hypothesis was not false..
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