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Are Type I or Type II Errors More Concerning?

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Is it more important for the researcher to be concerned with Type I or Type II errors in a study? Why do these error types only apply to the analysis of data sets, rather than individual instances?

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Solution Summary

In statistical analysis, do we care about type I or II errors more? In this solution we explore this concept in detail. A solid discussion of the differences between the two and exactly why one might be more dangerous than the other in an experiment is given.

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Both Type I and Type II errors can be quite serious depending on the context of the problem being studied. Type I errors (also known as false positives) can be the most serious, however. As opposed to type II (false negative), a type I error can result in the researcher becoming convinced of an unreal phenomenon. If this data is published as falsely positive, then it is likely that the researcher can face increased scrutiny ...

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