Final answer:
Violation of the assumptions underlying statistical tests can result in increased risk of Type I error and Type II error. Type I error occurs when the null hypothesis is rejected even though it is true, while Type II error occurs when the null hypothesis is not rejected even though it is false.
Step-by-step explanation:
The violation of assumptions underlying statistical tests can result in an increased risk of a Type I error. A Type I error occurs when the null hypothesis is rejected even though it is true. This is also known as a false positive. For example, if a medical test incorrectly diagnoses a healthy person as having a disease, it would be a Type I error.
A consequence of committing a Type II error is failing to reject the null hypothesis when it is false. This is also known as a false negative. It means that a real effect or difference exists, but the statistical test fails to detect it. For instance, if a new drug is effective but is not approved due to a statistical test failing to show its effectiveness, it would be a Type II error.