Final answer:
Type I error occurs when the null hypothesis is rejected, even though it is true. Type II error occurs when the null hypothesis is not rejected, even though it is false. Three common problems that can increase the chance of Type II error are: inadequate sample size, insufficient statistical power, and measurement error.
Step-by-step explanation:
Type I and Type II errors are two types of errors that can occur in hypothesis testing. Type I error occurs when the null hypothesis is rejected, even though it is true. This error is also known as a false positive. Type II error occurs when the null hypothesis is not rejected, even though it is false. This error is also known as a false negative.
There are three common problems that can increase the chance of Type II error:
- Inadequate sample size: A small sample size may not provide enough evidence to detect a true effect, leading to a higher chance of Type II error.
- Insufficient statistical power: Statistical power is the probability of correctly rejecting a false null hypothesis. A low statistical power decreases the chances of detecting a true effect, increasing the chance of Type II error.
- Measurement error: Inaccurate or imprecise measurements can introduce noise and variability into the data, reducing the ability to detect a true effect.