Final answer:
Type I error occurs when a true null hypothesis is rejected, while Type II error occurs when a false null hypothesis is not rejected. Sample size can be increased or the significance level adjusted to reduce these errors.
Step-by-step explanation:
In statistics, Type I and Type II errors are related to control charts. A Type I error occurs when a true null hypothesis is rejected, meaning that you conclude there is a difference or problem when there actually isn't. For example, if you conclude that a process is out of control when it is actually in control. This error is represented by the symbol α and is also known as a false positive.On the other hand, a Type II error occurs when a false null hypothesis is not rejected, meaning that you fail to detect a difference or problem when there actually is one. For example, if you conclude that a process is in control when it is actually out of control. This error is represented by the symbol β and is also known as a false negative.To reduce the chances of Type I and Type II errors, the sample size can be increased or the significance level can be adjusted. However, there is always a tradeoff between the two types of errors.