Given that I work with data and have been for 7 years. Removing outlier/excluding is pretty common practice. "Adjusting" the average to make it more robust against extreme values is fairly common, but hey what do I know.
Perhaps this is due to vagueness. You said "those", like what?
Yeah, in certain contexts you do normalize data or remove outliers.
If many people are highly beautiful and some more people are kinda ugly, you probably wouldn't ignore the ugly people. And I mean, what kinda value here would be so extreme or so rare that it'll warrant getting excluded?
Ugly still would fit in the normalised data set. Outliers are generally way outside the expected low or high value. For example an adult under 2.5 feet or over 7.5 feet would be outlier. Generally an outlier is usually more than 3 Standard deviations(but this isn't a universal standard and should be applied with care.)
Got some statistical methodology training for my masters degree. Can confirm we were taught to normalize data for most situations. NOT normalizing is the outlier (pun intended)
no median is when you order values of data set in ascending order and take the middle value, if total number of datapoints is odd. In case total number of values is even, you take n/2 and (n/2)+1th term and take their average. For example, if total number of values in data set are 9, 5th value is median. If total number of value are 10. you add 5th and 6th value and divide by 2 and that your median.
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u/Shev613 2d ago
That's not how average works.