Answer with explanation:
Outlier, in a data set are those real numbers called variates , if we consider two consecutive real numbers in that data set, when data is arranged in ascending or descending order the difference looks filthy,or ambiguous.
For, example , 13,45, 23.45, 35.45, 49.45,55.45, 80.45.
So, we can say that ,applying the above definition , 80.45 is an outlier.
Outliers affect the mean and Standard deviation.
→If you discard 50 % of the data , to get the better result in an experiment,the data set will become smaller and getting accurate result will be a task similar to catch a ruffian who is riding in a four wheeler and you are running behind him on footsteps.
→Similarly, Examining a smaller random sample of the population will not give accurate result.
Most of the Outliers lie either on right or left of Data set when arranged in ascending or descending order.
To overcome the effect of an outlier,we should consider large sample to get better result, as leaving some variates from right or left will have little effect on the result.
Option B: Examine a larger random sample of the population.
