by Jason Haley
With the three big winners in last night’s lottery splitting such a large pot, the income-level demographic data for where they live will be effected. However, depending on the way you look at this effect, there will either be slight changes if any, or there will be a large change. The reason for this difference is that demographic datasets supply both mean and median household incomes.
The lottery winners provide us with a great example of why we run median household income rather than average household income. This is because using a mean generally works well for data with normal distributions while medians are generally used on data with skewed distributions. And as you can probably guess, income data is quite the skewed dataset. Since a mean is so heavily influenced by outliers, we use a median. The median value will provide the value in the middle of the data (when sorted in ascending order).
To illustrate this, let’s pretend that one of the lottery winner’s home ZIP code has 10,000 people. Let’s also pretend that, by some kind of freak chance, every one of these people has an income of exactly $50,000. That would mean that that ZIP code has an average and median household income of $50,000. But, the lottery winner has now changed that. With their income changing from $50,000 to somewhere around $500,000,000, the new mean household income would be $99,995 and the median income would remain $50,000.