You have to read through the steps, so the question being asked is below (2nd page) I believe. Then go through the steps. Please explain clearly. Thank you!

QUESTION-3: HYPOTHESIS TESTING The idea of hypothesis testing in statistics is closely related to the concept of condence intervals. We can use hypothesis testing to either validify or refute an idea by using the limited data that is available after random sampling from a population. So what are the mechanics of hypothesis testing? The methodology can be summarized by the following steps: Step-1: Determine whether the hypothesis being tested requires a one sided or a two-sided test. In our lectures we discussed how we can do a twosided test. So below you will nd a question where you will do a two-sided test. But doing a one-sided test is just as simple. Step-2: Write the null hypothesis. This is the hypothesis that we will test. In the end, we will either reject or fail to reject this null hypothesis. Step-3: Write the alternative hypothesis. The burden of proof is always on the alternative hypothesis. So we begin with a position where we assume the null hypothesis is correct and then try to see whether we can reject the null hypothesis with the available data. Step-4: Set up the test statistic required for hypothesis testing. The test statistic is equal to: [Sample average value we are testing in the null hypothesis] / standard error of the sample Step-5: Find the critical value that is associated with the desired condence level. Please note that this critical value will depend on the sample size and the chosen condence level. If the sample size is less than 30 observations, you will have to look up the critical value from the t distribution table. t-distribution tables with one and two tails can be found here: httpswww.statisticshowto.com/tab[es/t-distributiontable/ If the sample size is greater than 30 observations, you can look up the critical value from standard normal distribution table, which can be found here: https://www.math.arizona.edu/~j watkinsormal-tablepdf Step-6: Compare the test statistic with the critical value. If the test statistic is greater than or equal to the critical value, we reject the null hypothesis. If it is less than the critical value, we fail to reject the null hypothesis. Step-6: Compare the test statistic with the critical value. If the test statistic is greater than or equal to the critical value, we reject the null hypothesis. If it is less than the critical value, we fail to reject the null hypothesis. Step-7: Interpret the result and state the probability of making a type I or a type 11 error. Type I error in statistics is the probability of rejecting a hypothesis we should not have rejected. Type 11 error is the probability of not rejecting a hypothesis we should have rejected. Please keep in mind that the test statistic we calculate depends on the random sample we select from the population. Hence, it can be the case that we reject a null hypothesis with the test statistic we calculate by using one random sample. Yet with another random sample and the test statistic we calculate by using that random sample, we might fail to reject the same null hypothesis. When we fail to reject a null hypothesis with a random sample at the 95% condence level, this should be interpreted as follows: If we do this random sampling experiment many times, 95% of the time the test statistic we calculate will be either bigger than or equal to the critical value associated with the desired condence level. But 5% of the time the random samples we collect will give us a test statistic that will be less than the critical value. Hence, if the value being tested in the null hypothesis is correct and if the condence level of the test is 95%, there is a 5% chance we will be making a Type I error if we fail to reject the null hypothesis. Similarly, if the value being tested in the null hypothesis is wrong and if the condence level is 95%, there is a 5% chance we will make a Type 11 error if we reject the null hypothesis. Now, we will make use of the infomiation above in the following question: The mean number of sick days an employee takes per year is believed to be about ten. Members of a personnel department do not believe this gure. They randomly survey eight employees. The number of sick days they took for the past year are as follows: 12, 4, 15, 3, 11, 8, 6, 8. Let x = the number of sick days they took for the past year. Should the personnel team believe that the mean number is ten? Use the steps int the pages above to test the null hypothesis of whether the mean number is equal to 10