As an example of Type 1 and Type 2 errors, let's say a person is on trial and only they know they are guilty or not guilty (see the table below). Ho True Ha True Do Not Reject Ho Correct Conclusion Incorrect Conclusion Type 2 Error Reject Ho Incorrect Conclusion Type 1 Error Correct Conclusion We'll let Ho=person guilty. Ha must be opposite of Ho, so Ha=person not guilty. Let's say the person is really guilty. Both sides present evidence and then the jury decides based on the evidence presented. If the jury fails to reject Ho, they come to the correct conclusion that the person is guilty. If the jury, on the other hand, rejects Ho and concludes the person is not guilty, the jury frees a guilty person and a Type 1 error results. Type 1 errors occur when we reject Ho when we should not. Now let's assume the person is really not guilty (Ha is true), and the jury rejects Ho and comes to the correct conclusion that the person is innocent. But what if the jury does not reject Ho and finds the person guilty, where an innocent person is convicted. This is an example of Type 2 error, where Ho is not rejected when it should be. Statistically speaking, we are generally agnostic to which is a bigger problem, type I (false positive) errors or type II (false negative) errors. However, in certain circumstances it may be important to try and put more emphasis on avoiding one or the other. Question Can you think of an example of where you may want to try harder to avoid one type or another? Can you think of a political, social, economic, etc. policy that pushes people toward avoiding one type or another? What are the repercussions of such policies