Suppose an inventor is interested in the proportion of local consumers who would be interested in purchasing her new product. If she samples local residents at random and tests hypotheses regarding p, the population proportion, what should she do to reduce her risk of making a Type II error?

a. Increase the number of local consumers she will sample

b. Decrease the number of local consumers she will sample

c. Make sure her sample of local consumers is exactly 10

d. Decrease the significance level

A type II error is sometimes called a beta error because it confirms an idea that should have been rejected, claiming the two observances are the same, even though they are different. A type II error is essentially a false positive.

A type II error can be reduced by making more stringent criteria for rejecting a null hypothesis such as:

Increasing the the sample size used in the Test: this is a strategy used to increase the power of the test and reduce the error to a considerable amount.
Increasing the significance level: choosing a higher level of significance is important for double checking and which increases accuracy.