Final answer:
The question deals with hypothesis testing, where null and alternative hypotheses are created to test against an observed sample mean in situations like online communication hours or study habits, using statistical distributions like the t-distribution when appropriate conditions are met.
Step-by-step explanation:
The question concerns generating hypotheses for a hypothesis test to determine if the proportion of college students who spend 10 or more hours per week communicating online is different from the 60% previously suggested. In this context, the null hypothesis (H0) typically states that there is no difference between the observed sample proportion and the population proportion (60% in this case), while the alternative hypothesis (H1) suggests that there is a statistically significant difference.
To test whether the study habits at a college match the national average, which suggests students study less than 20 hours a week, we would use the t-distribution since we have a small sample (n=25) and the population standard deviation is unknown.
When conducting a hypothesis test regarding the mean time students spend on the phone or doing homework, if the sample size is small (typically n < 30) and the population standard deviation is unknown, the appropriate distribution to use is the t-distribution. It is important to set up the null (H0) and alternative (H1) hypotheses correctly; typically, H0 contains the equality condition, and H1 reflects the research hypothesis that there is an increase or decrease.