Suppose the results indicate that the null hypothesis should not be rejected; thus, it is possible that a type II error has been committed. Given the type of error made in this situation, what could researchers do to reduce the risk of this error? Choose a 0.01 significance level, instead of a 0.05 significance level. Increase the sample size.

Increase the sample size.

Step-by-step explanation:

Increasing the sample size is the best way to reduce the likelihood of a type II error.

The type II error occurs when a hypothesis test accepts a false null hypothesis. That is, it fails to reject the null hypothesis that is false.

In such a situation, to increase the power of the test, you have to increase the sample size used in the test. The sampling size has the ability to detect the differences in a hypothesis test.

We have a bigger chance of capturing the difference if the sample size is larger, and it also increases the power of the test.

it would be 6xy and -16xy.

nope.   happens to me as well.

-aparri