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Web Performance & Core Web Vitals
What LCP, INP, and CLS each actually measure, why "good" is defined at the 75th percentile of real page loads rather than the average, and why a fast average can still hide a genuinely bad user experience.
What do LCP, INP, and CLS each measure, and why are all three needed instead of one overall "speed" number?
LCP (Largest Contentful Paint) measures loading performance, specifically how quickly the largest visible element renders, "good" is 2.5 seconds or less. INP (Interaction to Next Paint) measures interactivity/responsiveness, the delay between a user interaction and the browser's next visual response, "good" is 200 milliseconds or less. CLS (Cumulative Layout Shift) measures visual stability, how much content unexpectedly shifts around during the page's lifecycle, "good" is a score of 0.1 or less. A single overall number couldn't distinguish a page that loads fast but jumps around from one that's stable but slow to interact with, three separate metrics are needed because loading, interactivity, and stability are genuinely different failure modes.
Why does Google measure Core Web Vitals at the 75th percentile of real page loads instead of using the average?
An average can be pulled down by a large number of fast loads on good connections and powerful devices while completely hiding a meaningful tail of slow, frustrating experiences on weaker devices or networks, real users don't experience "the average," they experience their own specific load. Measuring at the 75th percentile means a page only passes if at least three out of four real page loads actually meet the threshold, which is a much more honest bar for "most users get a genuinely good experience" than an average that a handful of very fast loads could distort.
A page has a fast average LCP but users still frequently report the page feeling slow. What could the percentile-based measurement reveal that an average wouldn't?
If a substantial slice of real page loads, say the slowest 25%, badly miss the 2.5 second LCP threshold (a slow connection, a busy device, a cold cache), the average can still look fine because it's dominated by the faster majority, while a real, sizable group of users are having a genuinely bad experience the average is actively hiding. Checking the 75th-percentile value directly (rather than the mean) surfaces that gap: if the 75th percentile is well above 2.5 seconds even though the average looks fine, that's a concrete signal that meaningful numbers of real users are missing the threshold, not a false alarm.