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Nature of the observed data and mild and extreme outliers

This question is open to interpretation - I think it's supposed to be a tricky question so I'm not entirely sure where it's going. We aren't actually given any data.

Assuming data normally distributed:

Q 1 Theoretically, does the nature of the observed data have an effect on the definition of what is called "mild" and extreme" outliers?

I don't have any idea what is meant by "nature" is question 1. Discrete/continuous, perhaps? If so, how?

Q 2 What percentage of values will be "mild" or "extreme" outliers for the observed data?

It seems to me that you can use the definition of 1.5 IQR and 3 IQR to define where the limit between them is by using a Z table. If the mean of a sample is 100 with a SD of 10, then the IQR = 0.675*2 Standard deviations, or 13.5. Therefore, the limit of mild outliers is 6.75 + 13.5*1.5 = 27. So values less than 73 or greater than 127 would be extreme.

BUT - While you can use the assumption of normal distribution to find the probability of an actual outlier, I don't see that you can make the kind of prediction suggested by Question 2 above.

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This question is open to interpretation - I think it's supposed to be a tricky question so I'm not entirely sure where it's going. We aren't actually given any data.

Assuming data normally distributed:

Q 1 Theoretically, does the nature of the observed data have an effect on the definition of what is called "mild" and extreme" outliers?

I don't have any idea what is meant by "nature" is question 1. Discrete/continuous, perhaps? If so, how?

Yes, it does because the spread or dispersion of data about the mean will be small if the standard deviation is small, and it will be ...

Solution Summary

The nature of the observed data and mild/extreme outliers are determined.

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