Quantitative data

Quantitative data is numerical data that can be counted or measured. It's any data that can be quantified and has a numerical value. This data is calculable and can always be verified.


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What are the different types of quantitative data collection methods?

These methods provide quantifiable human insight to further enrich your user research. 

  1. Surveys (ratings, ranking, scales, and closed-ended questions).

Surveys gather data from a representative group of people, usually to understand a larger population. Surveys with large numbers of contributors collect quantitative feedback. When executed well, surveys can be an efficient method to connect with customers or your target audience to gain a snapshot of their successes or struggles with a product or service.

      2. A/B testing

A/B testing involves presenting users with two versions of a design to see which one drives more conversions and quantifiable metrics. 

      3. Web analytics

Web analytics is all about quantifiable data. This data help track where users tend to click on a screen, offers a glimpse of where you should dig deeper, highlight common behaviors, and showcase any pain points users have. 

What are the benefits of quantitative data?

When using quantifiable data, there's a lesser chance of there being bias in your data. This data is objective, and the numbers are verifiable, limiting confusion on the data's accuracy. 

This data is also less time-consuming and cheaper to conduct. It tends to be easier to send numerical surveys to people rather than gather a sample group of testers to conduct interviews. 

What are the disadvantages of quantitative data?

Although quantitative data is great for numerical insights, researchers have limited information about users' actual perceptions. Researchers usually can't ask for detailed feedback from users, there's limited context, and they may be left confused on a specific metric.