Answer:
The Type I error that could result from the test is that The test provides convincing evidence that the proportion is greater than 90%, but the actual proportion is equal to 90%.
Step-by-step explanation:
We are given that machines at a factory produce circular washers with a specified diameter.
The quality control manager at the factory periodically tests a random sample of washers to be sure that greater than 90 percent of the washers are produced with the specified diameter.
<u><em>Let p = proportion of all washers produced with the specified diameter</em></u>
So, Null hypothesis, : p = 90%
Alternate Hypothesis, : p > 90%
Here, null hypothesis states that the proportion of all washers produced with the specified diameter is equal to 90%.
On the other hand, alternate hypothesis states that the proportion of all washers produced with the specified diameter is greater than 90%.
Now, Type I error states the Probability of rejecting null hypothesis given the fact that null hypothesis was true or in other words Probability of rejecting a true hypothesis.
So, according to our question Type I error would be that the test provides convincing evidence that the proportion is greater than 90%, but the actual proportion is equal to 90%.