Hypothesis testing and errors are important elements in statistics. Let's take a look at an example of different types of statistical possibilities:

A person is on trial for first-degree murder. The jury comes to a decision. There are four possible outcomes:

1. The person isinnocentof first-degree murder, and the jury declares the personinnocentof first-degree murder.
2. The person isinnocent of first-degree murder, and the jury declares the personguiltyof first-degree murder.
3. The person isguiltyof first-degree murder, and the jury declares the person asinnocentof first-degree murder.
4. The person isguiltyof first-degree murder, and the jury declares the person asguiltyof first-degree murder.

This is an example of Type I and Type II errors using the null and alternative hypotheses we arestudyingthis week.Use the table below as a guide

	Reality
H0is True Null Hypothesis is True	H1is True Null Hypothesis is False
Conclusion	Fail to Reject Null Hypothesis	Correct Conclusion (True Negative)	Type II Error (False Negative)
Reject Null Hypothesis	Type I Error (False Positive)	Correct Conclusion (True Positive)

In your initial discussion post:

• Come up with your own scenario and identify the four possible outcomesfor your scenario.
• For each outcome, identify which represents a Type I error, and which represents a Type II error.
• Identify the null and alternative hypotheses.

• What is the impact of a Type I error on the scenario described?
• What is the impact of a Type II error on the scenario described?