Let’s stop here and recap:

• Individual experiences are just data points along a distribution. What feels fast depends on the conditions under which it was experienced. Everyone’s conditions are different.
• Lab tests may not be configured to be representative of the most common experiences on the curve, or any experience on the curve for that matter.
• User-centric metrics require extra care to ensure that behaviors are emulated faithfully in the lab.

[…] An even better solution would be to build stronger data bridges between field and lab tools, so that the lab tool itself can make informed recommendations about the most realistic user profiles to simulate.