Social Media Metrics For Dummies

Knowing What to Do with Outliers

An outlier is any data point that doesn’t fit into your established average data pattern. By paying attention to your outliers, you can see where to position yourself by focusing on which outliers are repeating data and which ones start to multiply, indicating a trend.

There are two types of outliers in data:

Outliers caused by errors in measurement or data.

Outliers caused by the first indicator of long-tail changes in your measurement sets. (Long-tail is a marketing term referring to results shown over time.)

Outliers caused by errors

To deal with the first type of outlier (an error that you want to eliminate before it corrupts your data), you need to eliminate it. Eliminating the outlier requires several steps, the first of which is exporting your data to a spreadsheet:

1. Sort your spreadsheet data by ascending order.

2. Locate your median number.

The median number of 3 and 4, for example, is 3.5.

3. Find the point at which 25 percent of the metrics in your spreadsheet are larger.

This is called the upper quartile, or Q2.

4. Find the point at which 25 percent of the metrics in your spreadsheet are smaller.

This is called the lower quartile, or Q1.

5. Subtract Q1 from Q2.

The result is the interquartile range. You need this range to exclude ...