Understanding Outliers

NitroIQ naturally draws your attention to outliers in your data. Normally we will highlight Sprints or Issues that are considered to be statistical outliers.

Why Outliers Matter

Outliers can be an indicator of a problem with the issue or the team's process.

For example, if an issue has a large number of status change events, it may be indicative of a problem with the issue itself, ie. the issue may be too large or too complex, the team member may not be breaking down work into small enough chunks, etc...

Highlighted outliers will be presented as colored tags in data tables.

How Outliers are Computed

NitroIQ uses the Z-Score method to compute outliers. Any data point that is 1 std. deviation away from the mean, is considered an outlier.

The z-score (z = (x - mean) / standard deviation) measures how many standard deviations a data point is away from the mean. Data points with a z-score beyond a certain threshold (e.g., 1) are considered outliers.

For example, the table below highlights two sprints that are outliers with both a high and low mean status change count compared to other sprints.

In the example above, two sprints have had an unusual number of status change events for the issues assigned to them.