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Respond to the following in a minimum of 175 words:
This week, we consider how to conduct hypotheses test on one sample data. Discuss the concepts associated with these tests. Consider the following:
- The difference between a one tail and a two tailed test.
- The importance of stating the null and alternative hypotheses before conducting the test.
- The importance of a type one error (p) in conducting the test
- The relationship between the p value and our decision to accept or reject the null hypothesis
According to Black (2017), the purpose of stating the null and alternative hypothesis when conducting hypothesis testing is to provide a formal structure for including all possible outcomes within the experiment. The null hypothesis suggests that current theories, standards, and systems continue to exist while the alternative hypothesis suggests there is something new occurring within the system. This formal structure allows the researcher to clearly determine whether there is “proof” of their theory based on whether the null or alternative hypothesis is found to be true upon conclusion of the study. Statistical hypothesis testing is conducted using either a one or two-tailed test approach, where a two-tailed test includes both sides of the distribution while a one-tailed test only includes one side of the distribution (Black, 2017). There is both the greater than and less than possibility for a two-tailed test, while a one-tailed test requires that the resulting p value must be either greater than or less than, but not both. The alternative hypothesis is accepted if the p value has the proposed relationship, while the null hypothesis is accepted if the p value does not fall within the range indicated by the alternative hypothesis.
Black (2017) describes a Type 1 error as one where the researcher rejects a null hypothesis when it is actually true, and can occur if the random sample happens to consist primarily of outliers versus data that is concentrated around the mean. To try and avoid a Type 1 error, the alpha level of signifiance is determined before hypothesis testing begins, and on the graph represents the rejection region beyond critical values. One value which is commonly used as the level of significance for testing is 0.5 (Black, 2017).
Black, K. (2017). Business Statistics: For Contemporary Decision Making, (9th Edition). Hoboken, NJ: Wiley.
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Marcus and Class, here is a link which might help you gain a better understanding of Hypothesis Testing and Type 1 and 2 Errors.
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