Final answer:
To determine the effectiveness of a drug using a hypothesis test, we define the null and alternative hypotheses, calculate the test statistic and corresponding p-value, and compare the p-value with the significance level. Without comparison data, the test cannot be completed, but if the p-value were below 0.01, the drug could be considered effective.
Step-by-step explanation:
Using a Significance Level to Test Effectiveness of a Drug
When conducting a hypothesis test to examine the effectiveness of a drug, we follow these steps:
- Define the null hypothesis (H₀) and alternative hypothesis (H₁). The null hypothesis generally states there is no effect or a difference equal to a certain value, while the alternative hypothesis states there is an effect or a difference that is not equal to the value specified in the null hypothesis.
- Calculate the test statistic, which, in medical trials, often involves proportions and is typically a z-score.
- Determine the corresponding p-value for the test statistic and compare it with the significance level to decide whether to reject H₀.
- If the p-value is less than the significance level, we reject H₀. If the p-value is higher, we fail to reject H₀, meaning there isn't sufficient evidence to support H₁.
In the given example, you would compare the proportion of patients who developed nausea after taking the drug with either the proportion expected under no treatment or a standard treatment to assess the drug's effectiveness. However, the problem does not provide such comparison data.
Due to the lack of explicit comparison numbers, we cannot complete the hypothesis test. However, if given those numbers and if the calculated p-value were less than the significance level (0.01), we would conclude that the drug is effective in reducing nausea (hypothesis test outcome).