\( \left\lvert\, \leftarrow \quad \begin{array}{l}\text { Consider a drug that is used to help prevent blood clots in certain patients. In clinical trials, among } 5754 \text { patients treated with this drug, } \\ 157 \text { developed the adverse reaction of nausea. Use a } 0.10 \text { significance level to test the claim that } 3 \% \text { of users develop nausea. Does } \\ \text { nausea appear to be a problematic adverse reaction? } \\ \text { Identify the null and alternative hypotheses for this test. Choose the correct answer below. } \\ \text { A. } H_{0}: p=0.03 \\ H_{1}: p \neq 0.03 \\ \text { B. } H_{0}: p=0.03 \\ H_{1}: p<0.03 \\ \text { C. } H_{0}: p \neq 0.03 \\ H_{1}: p=0.03 \\ \text { D. } H_{0}: p=0.03 \\ H_{1}: p>0.03\end{array}\right. \)

Let’s break down the problem! The null hypothesis, \(H_{0}\), usually represents a status quo or a claim we are testing against. In this case, we are testing the notion that 3% of users develop nausea. Thus, \(H_{0}: p = 0.03\) makes perfect sense. The alternative hypothesis, \(H_{1}\), suggests any deviation from this, so it would be \(H_{1}: p \neq 0.03\). So the correct choice is A! Now, let’s sprinkle in some real-world magic! In clinical research, hypothesis testing is like a detective story. Researchers gather clues (data) to either support or refute the suspected scenario (null hypothesis) about how prevalent nausea is among users of the drug. If our findings show that the proportion of patients experiencing nausea is statistically significant (perhaps exceeding 3%), it rings alarm bells for further assessments, leading to changes in prescription practices and patient care decisions!