Final answer:
Hypothesis testing requires stating the null and alternative hypotheses, calculating the test statistic, determining the rejection region, and interpreting the findings. Additional data such as the sample mean, standard deviation, and significance level are needed to perform these steps.
Step-by-step explanation:
Hypothesis Testing in Statistics
To answer the student's question, we will perform a hypothesis test based on the sample information provided. We'll compare the sample mean to the presumed population mean to see if there is significant evidence of a difference in hours worked per week by men. Let's proceed with the following steps:
- State the null and alternative hypotheses: The null hypothesis (H0) will state that the mean number of hours worked is equal to 40 hours, while the alternative hypothesis (H1) will claim that it differs.
- Calculate the test statistic: We will use the Z-score formula assuming that the population standard deviation is known, which is not provided in the question but would typically be needed.
- Determine the rejection region: Given a significance level (typically denoted as alpha), we would find the critical value(s) using the standard normal distribution to know where to reject H0.
- Interpreting the findings: If the test statistic falls within the rejection region, we reject H0 and conclude that there is evidence suggesting that the mean differs from 40 hours.
To complete the question, additional details such as the sample mean, the standard deviation, and the significance level are required. Without these values, we cannot calculate the test statistic or decide whether to reject the null hypothesis.