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Asked Jun 4, 2019

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The book states the following: "The smaller our sample size and the more extreme the outlier score is, the more these extreme scores will affect the mean in a skewed distribution"

Explain how this occurs. I.e. How does a small sample size increase the effect of an outlier on the mean in a skewed distribution? How do more extreme scores affect the mean in a skewed distribution?

Step 1

We know from equation 1 below that the width of confidence interval is inversely proportional to the square root of sample size n. This would mean that the confidence interval will widen as sample size decreases. As ouliers tend to increase the standard deviation, they would also widen the confidence interval because it's directly poportional to sigma or sample standard deviatio...

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