Final answer:
The question involves hypothesis testing and the test of independence in statistics, where the null hypothesis states that two variables are independent, and the alternative suggests they are not. Statisticians compare the p-value from the test to a significance level to decide whether to reject the null hypothesis.
Step-by-step explanation:
The question pertains to hypothesis testing in statistics, specifically testing for independence in a contingency table context. When looking at the influence of phone calls compared to flyers on voters, or any two categorical variables, statisticians use a test of independence to determine whether there is a significant association between the two variables. The null hypothesis (H0) usually posits that there is no association between the variables (they are independent), and the alternative hypothesis (H1) suggests that there is an association (they are not independent).
For example, if we are considering cell phone usage while driving and receiving a speeding violation, the null hypothesis states that being a cell phone user while driving and receiving a speeding violation are independent events. If this were true, a certain number (like 28 expected users) would be predicted based on the assumption of independence. However, if the observed data significantly deviates from this expectation, then the null hypothesis may be rejected in favor of the alternative hypothesis, indicating that a relationship exists between the two variables.
Determining whether to reject the null hypothesis often involves setting a significance level, denoted as alpha (α), and comparing it with the computed p-value from the test. If the p-value is less than alpha, then there is sufficient evidence to reject the null hypothesis in favor of the alternative hypothesis.