Statistics have value if interpreted correctly.

But we all know statistics can be manipulated as well. You can report statistics that are 100% true, yet interpret them to provide a misleading, or often opposite conclusion than the truth.

Here is a common statistical interpretation error:

A couple has three kids. One is 2 feet tall, one is 3 feet tall, and one is 4 feet tall.

The father, a statistician, makes note that **24 + 36 + 48 = 108**, so he realizes his kids are an average of **36** inches, or 3 feet in height ( **108 / 3** ).

The next year, the kids have each grown 4 inches, and they have a new baby, who is **20** inches in height.

Now, the father calculates that **20 + 28 + 40 + 52** = **140** inches, and 140 / 4 = **35**.

What??!?

So in one year, his kids have gone from an average of **36** inches to an average of **35** inches!

The father calls the doctor in a panic, yelling **“The kids are shrinking! The kids are shrinking!”**

The truth is, that the kids are not actually shrinking. The sample has changed.

Concluding that their kids have shrunk an average of 1/4″ each is an invalid way to explain the drop from 36 to 35 inches in their kids’ average height.

The true interpretation is, that because of the addition of a new baby that is shorter than the rest, the average height statistic has been skewed. The truth is that **all kids are actually growing** in height.

This is the invalid method used by every “rich get richer, poor get poorer” study ever done. The study does not follow individual households (as the study I cite here does), and makes the false assumption that rich and poor people are the same people over a span of time (which my article shows is usually false).

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