Outliers

Descriptive statistics are expected to summarize the data, which means they should give you a feel of what the data looks like without having to sift through all the values.

Does Mean live up to this expectation? Let's test.

Looking at the values and comparing them with the mean we calculated, it seems to be doing the job. After all, the values are not too off compared to the mean.

Now for some fun. What if we allow a sumo wrestler, Yamamotoyama, who lost his way in town, to board the bus? His weight happens to be 272 Kg.

Once he boarded the bus, we tried calculating the mean again.

The mean, in this case, jumps to a whopping 110 Kg. The Mean no longer lives up to the expectations, as none of the values are quite as close to the mean, not even that of Yamamotoyama.

Lesson learned? Mean is only good when there are no outliers.

In this example, our Sumo friend Yamamotoyama is an outlier, one that is capable of throwing Mean out of the window with all his might.