Skew, in layman’s terms, means that data is distorted. The data points don’t fall evenly around the center of a distribution.
Consider this example. Assume ten people are in a room, and you want to know what their average net worth is. If this was a typical cross section of America, the number would be $53,100
Now imagine Microsoft’s Bill Gates was one of those people. He would skew the results. The average net worth would jump to a few billion.
In statistical terms, ‘skew’ has a specific meaning. A skewed distribution has a longer tail on one side of distribution curve. A positive skew has a longer right tail; a negative skew has a longer left tail. Skewness is the measure of the asymmetry of the distribution.
For laypeople, though, most people use the term ‘skewed’ to mean that the data is distorted or has outliers. Simply identifying that the data has a skew to it is often enough to get an observer to dive deeper into the problem. Identifying that asymmetry when an even result is expected is a red flag. The obvious next step is to dive into the root cause analysis to identify the factors that skewed the output.