Hypothesis Testing
Question 1
Explain when a z-test would be appropriate over a t-test.
Z -test is a statistical test mostly used where normal distribution is applied and usually used in an attempt to solve problems that contains sample whose n value is more than 30 in this case n ≥ 30. On the other hand, t-test works best for any statistical hypothesis tests in which t test analysis and statistics follows the distribution on a hypothesis that is null. In most cases, t-test is used when a comparison of two samples is done in even a situation whereby they have different numerical of replicates. In this case, t-test would not fit large values as z-test.
An illustration of an example to show z-test is when a person wants to test if coffee and tea are equally popular or dunk in an area or town under study. He will then take a sample of like 400 of which 320 drink tea. In testing the hypothesis, z-test would be appropriate.
Question 2
Researchers routinely choose an alpha level of 0.05 for testing their hypotheses. What are some experiments for which you might want a lower alpha level (e.g., 0.01)? What are some situations in which you might accept a higher level (e.g., 0.1)?
Lower alpha equals to lower probability usually of type 1 error. This means that there will be dire consequences of rejecting true or null hypothesis. A good example is on the court where jury trial whereby null refers to innocence. When you reject the null then you mean one is to be convicted.
Situations where higher level of alpha let’s say 0.1 works when accepting null hypothesis.
Question 3
If you were the researcher for this study, would you immediately set the alpha level at 0.001 or 0.05? Why or why not? (Read the methodology below)
I would set alpha to be 0.05, this is because I would expect to get results of the tests on the patients brought to the hospital.
