Final answer:
The hypothesis test at a 0.01 significance level involves stating a null hypothesis (H0) and an alternative hypothesis (H1) and then using sample data to determine if there is sufficient evidence to reject H0. If the p-value is less than 0.01, the null hypothesis is rejected.
Step-by-step explanation:
The question concerns a hypothesis test about a proportion in the subject of statistics, which is a branch of mathematics. First, we need to state the null hypothesis (H0) and the alternative hypothesis (H1) for this situation:
- H0: p ≥ 0.42 (The proportion of computer chips that fail is at least 42%.)
- H1: p < 0.42 (The proportion of computer chips that fail is less than 42%.)
To find out if there is sufficient evidence to support the company's claim at the 0.01 significance level, we would typically perform a hypothesis test using the sample data provided. This would involve calculating the test statistic for the given sample proportion (in this case, 39%) and comparing it to the critical value or using the p-value approach to determine if we can reject the null hypothesis.
If the calculated p-value is less than the alpha level of 0.01, we would reject the null hypothesis and conclude that there is enough evidence to support the company's claim that less than 42% of the computer chips fail in the first 1000 hours of their use.
The complete question is: A sample of 1300 computer chips revealed that 39 % of the chips fail in the first 1000 hours of their use. The company's promotional literature claimed that less than 42 % fail in the first 1000 hours of their use. Is there sufficient evidence at the 0.01 level to support the company's claim? State the null and alternative hypotheses for the above scenario. is: