Final answer:
Making a Type II error in this case would mean failing to reject the null hypothesis when there is actually a significant difference in the success rate between longer and shorter tears. There is not enough information provided to determine whether there is evidence that the success rate is greater for longer tears.
Step-by-step explanation:
Making a Type II error in this case would mean failing to reject the null hypothesis when there is actually a significant difference in the success rate between longer and shorter tears. In other words, it would mean concluding that there is no evidence of a greater success rate for longer tears when there actually is. This would be a false negative result, leading to a missed opportunity to identify and utilize a potentially more effective treatment for longer tears.
Based on the given information, we can perform a hypothesis test using alpha (α) equal to 0.05. The null hypothesis (H0) would be that there is no difference in success rates between longer and shorter tears, while the alternative hypothesis (Ha) would be that the success rate is greater for longer tears. We can calculate the p-value to determine the strength of the evidence against the null hypothesis. If the p-value is less than α, we would reject the null hypothesis and conclude that there is evidence of a greater success rate for longer tears.
However, without the actual p-value or more information about the specific test conducted and the sample size, we cannot determine whether there is sufficient evidence to support the claim that the success rate is greater for longer tears.