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Statistics False Positive or False Negative: Bigger problem?

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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 policy; political, economic, social, or otherwise, that pushes people toward avoiding one type or another? What are the repercussions of such policies?

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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 policy; political, economic, social, or otherwise, that pushes people toward avoiding one type or another? What are the repercussions of such policies?

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Statistically speaking, we are generally agnostic to which is a bigger problem, type I ...

Solution Summary

Your tutorial is 310 words and gives several examples for both questions.

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See Also This Related BrainMass Solution

Type I or Type II errors?

1) What is more important for the researcher to be concerned about in a study, Type I or Type II errors? Why? How does the type of data collected and the way in which the data is collected affect the possibility of Type I or Type II error?

2) Give two examples when practical significance would outweigh statistical significance. Explain your rationale.

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