As an example of Type 1 and Type 2 errors, let\'s say a person is on trial and o
ID: 3150534 • Letter: A
Question
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.
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 health related policy (or political, social, economic, etc. policy) that pushes people toward avoiding one type or another?
What are the repercussions of such policies?
Ho True
Ha True
Do Not Reject Ho
Correct Conclusion
Incorrect Conclusion Type 2 Error
Reject Ho
Incorrect Conclusion Type 1 Error
Correct Conclusion
Explanation / Answer
In an experiment, where the null hypothesis says that post operative patients of kidney operation leads to bleeding, when actually successful operation do not lead to bleeding, the researcher commits Type II error.
The null hypothesis says that Pramedical practitioners are earning less than to junior doctors with MBBS degree. But one rejects the null hypothesis to conclude that paramedics earn euivalent or moreto junior doctors, where in fact the null hypothesis is true. The researcher commits Type I error.
The Type I error might lead to rethink the earning capabilities and potential of paramedics.
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