Posted: August 19th, 2021

150 words agree or disagree to each question

Q1Studies are conducted to gather data in support or opposition of a prediction amongst variables. The null hypothesis is the assumption that there is no difference between the variables, whereas the alternative hypothesis is in support of the prediction you want to validate. A Type I Error is made when the null hypothesis is true, but incorrectly rejected. A Type II Error is made when the alternative hypothesis is true, but incorrectly accepted.

For example, consider a business that wants to test the effectiveness of a new advertising method. The null hypothesis is that the new advertising method has no effect or a negative effect on consumer behavior. The alternative hypothesis is that the new advertising method has a positive effect on consumer behavior. The business will have a Type I Error if it concludes that the new advertising method has a positive effect when it actually did not. This false positive may be due to incorrect calculations or other factors not considered, like pricing or customer service. The business will have a Type II Error if it concludes that the new advertising method did not have a positive effect, when it actually did. This false negative may also be due to incorrect calculations or misinterpreted improvements.

Q2

Before conducting a hypothesis test, it is necessary to have accurate data to determine how a population is/would perform. There are two types of hypotheses to use: the null hypothesis and an alternative hypothesis. A null hypothesis is when one uses data analyzed to make a claim, while an alternative hypothesis would be the desired outcome that would question the null hypothesis (Albright and Winston, 2017). Regardless of the management’s decision, one will either accept or reject the null hypothesis resulting in an error (Albright and Winston, 2017). There are two types of errors that could occur based on the decision made by management. The first type of error that can occur is when one rejects a true null hypothesis, which creates a type I error, and a type II error occurs when one does not reject a false null hypothesis (Albright and Winston, 2017).

A real-world example would be conducting hypothesis testing on student performance based on an old curriculum versus a new curriculum that a school implements. In this case, the faculty may be happier with the new curriculum, and additionally, may believe that student performance is better. The opposite of this would be the null hypothesis: the old curriculum was as good as the new curriculum, and students’ performance was very similar. The overall faculty experience and knowledge would make the alternative hypothesis making it their claim. However, the school can conduct end-of-course critiques, analyze student test results based on the old and the new curriculum, and conclude which curriculum was more effective for retaining information and engaging; this would be the null hypothesis.

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