A large outlier could affect the data set because it could make the mean larger, which just means that the average number of the set would be a lot bigger than it already is.
An example would be: 1, 3, 5, 19
19 is obviously the large outlier, so to find the mean, we would add everything together then divide by 4.
The mean would be 28/4=7 So 7 is our mean, but if the large outlier were, let's say 50, then the mean would be way larger than it already is.
