Quantitative tree testing is used for benchmarking and comparison. This method requires a larger sample size and provides precise measurements, such as how long it takes users to find a specific resource and the success rate of users navigating through the IA. Quantitative tree testing doesn't delve deeply into the reasons behind users' actions but offers valuable data for comparing different navigation structures. It's particularly useful when you have multiple IA options or are benchmarking against competitors or future redesigns.

We recommend beginning a quantitative tree test with a small qualitative study for two reasons:

• Piloting your study design: Ensure that your tasks are clear and do not bias participants. It'll help identify any issues in your test design that could affect the reliability of your quantitative results.
• Gaining deeper insights: Obtain insights about why users chose the answers they did, which category labels are confusing, and ideas for how to address these issues. Understanding the "why" behind user choices can help refine your IA before conducting the larger quantitative test.