Final answer:
The question pertains to hypothesis testing in statistics, where the goal is to determine if there is sufficient evidence to dispute a company's claim about the failure rate of computer chips. The null hypothesis states the company's claim is true (p = 0.63), and the alternative hypothesis states that the true failure rate is different (p ≠ 0.63). A hypothesis test using the sample data will help decide if the null hypothesis can be rejected at the 0.01 significance level.
Step-by-step explanation:
The subject of this question is Mathematics, specifically within the area of statistics. We are addressing a hypothesis testing scenario at the 0.01 significance level.
The null hypothesis (H0) is a statement that there is no effect or no difference, and it would be that the true proportion of computer chips that fail in the first 1000 hours is equal to the company's claimed proportion. Mathematically, we can express it as H0: p = 0.63, where p is the true proportion of failed chips.
The alternative hypothesis (H1) is a statement that indicates a change or difference. In this case, since we are disputing the company's claim, the alternative hypothesis would suggest that the true proportion of failed chips is different from the claim. It is expressed as H1: p ≠ 0.63.
To determine if there is sufficient evidence to dispute the company's claim, we would perform a hypothesis test using the sample data. The test would involve calculating a test statistic and comparing it to a critical value or using a p-value approach to see if the observed proportion of 0.59 significantly differs from the company's claimed proportion of 0.63 at the 0.01 significance level.