During an experiment, what two types of data results should be collected?

Collecting both quantitative and qualitative data during an experiment provides a comprehensive understanding of the results. Quantitative data consists of measurable, numerical values that allow for statistical analysis. This type of data is crucial for identifying trends, making comparisons, and validating results through objective measures. For instance, in a product design context, quantitative data might include metrics such as user engagement rates or conversion statistics.

On the other hand, qualitative data offers insights into experiences, opinions, and behaviors. It explores the 'why' behind the numbers, providing context and depth to the findings. This could involve user interviews, open-ended survey responses, or observational notes that capture how users feel about a product or service.

Integrating both types of data allows for a more nuanced interpretation of results. While quantitative data may show that a product feature is popular, qualitative feedback can explain the reasons behind that preference, revealing user motivations and needs. This dual approach is essential in the Design for Delight practice, as it helps teams create innovative solutions that resonate with users on multiple levels.