The fourth instalment in our Core Web Vitals series aims to provide insight into how to read the different types of data and the tools used for debugging. If you’re new to this series, check out the previous posts: An introduction to Google’s Core Web Vitals, Core Web Vitals Metrics, and Measuring Core Web Vitals.
Tools that monitor Core Web Vitals metrics, such as PageSpeed Insights are invaluable for improving overall website experience and performance but can provide different results during testing. The most common variance in scores can come down to two different types of data, lab data and field data.
What is lab data?
Lab data can be described as a predefined set of network and device conditions, assessed from a single location. An example of a testing tool that uses Lab data is Lighthouse. Lighthouse aims to provide consistent results by running tests through the same device, with the same performance, network speed and location each time in order to provide reliable results.
What is field data?
Field data, also known as Real User Monitoring (RUM) is based on real-user visits. It measures a given set of performance metrics for each individual user. Real devices, network conditions and geographic locations are all measurement metrics that make their way into the report. Tools that report to Core Web Vitals, usually do so using the 75th percentile.
The most important thing to understand about field data is that it is not just one number, it’s a distribution of numbers. That is, for some people who visit your site, it may load very quickly, while for others it may load very slowly. The field data for your site is the complete set of all performance data collected from your users.
So what’s the difference?
Field data is based on a wide variety of devices, networks and geographic varieties, from real users, whereas lab data creates a simulated user, constrained to a single device, usually with a throttled connection in a specific location.
With these differences in mind, lab and field data can provide quite different results. When debugging, although it’s always best to prioritise field data, it’s important to take into account both sets of results. Lab data can successfully fill the gap for users who may not successfully load your site and can allow you to fine-tune your site for users with lower-end devices or slower connections.
The end goal is always to improve the user experience, making each type of data invaluable in measuring and improving website performance.
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