The following list shows three factors that can either increase or decrease the probability of a Type II error. The sample size is increased. The significance level is increased. The standard error is increased. Which factors alone will cause the probability of a Type II error to increase?

Standard Error is increased.

Step-by-step explanation:

Type 2 error is failure to reject a false null hypothesis. This is a 'false negative' result of hypothesis testing, represented by beta 'β'.

Power of test is 1 - β, which shows probability of accurately stating  a null hypothesis false, when it is actually false.

Increased sample size increases the power of test accuracy, hence reduces type 2 error. Increased level of significance implies higher probability of rejecting a true null hypothesis, ie type 1 (false positive) error. So, it decreases type 2 error.

Standard Error increase implies lower level of t statistic , ie = (x - u) / (s / √ n) Lower the t value, higher is the probability of not rejecting null hypothesis, which implies higher chance of type 2 error.