I have a data set that includes the number of visits to a website. Here are some descriptive statistics for my data
Clearly, there are some huge values (individuals who have visited the site thousands of times.) To remove these outliers I considered simply removing any data that falls outside more than 3.5 standard deviations from the mean. The result is that I discovered there is still a significant fat tail with my data. After removal of data that falls outside more than 3.5 standard deviations my descriptive statistics adjust to
I also explored using a winsorized mean but again since my data is asymmetric I feel like my descriptive statistics are biased. Is there a method that I can use to reevaluate my data to provide descriptive statistics that would represent a ‘majority’ of the population?
As I understand the concept of bootstrapping, I could sample my population and then resample and generation thousands of populations that may represent my population differently based on the resample of my original sample population. Would this method be appropriate?
Any other ideas or direction?
Any references or examples with R would be very much appreciated as well.