If you’re using an online testing tool, you’ll probably need to do some data clean-up before analyzing your results.
Overall scores (based on factors such as success rate, directness, and speed) give you a quick idea of your tree’s effectiveness and how it compares to other trees.
Task scores will show you where your tree is performing well or poorly.
Low task success rates show you where your participants got confused, disagreed about where the answer would be found, or were lured down the wrong path by a promising (but wrong) intermediate heading.
First clicks are especially important to analyze, because they greatly affect success rates.
Finding where people backtracked helps you find evil attractors and dead ends.
Spotting areas of your tree that slowed participants shows you where subheadings need to be clarified.
Look for patterns of behavior across several related tasks.
Look for patterns of success or failure across different sections of the tree.
You can compare the results of different user groups (or other criteria) if you run separate tests for each, or if you run a single test and filter results by a corresponding survey question.
Next: Chapter 13 - Communicating results