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.

when studying polynomials, you often hear the terms zeros, roots, factors and x x the multiplicity of a zero is important because it tells us how the graph of the let's look at the graph of a function that has the same zeros

step-by-step explanation: