We control sample bias by having a diverse data panel to get a better representation of all people. Our data panel includes tens of millions of unique devices and is comprised of apps in every bucket. After receiving the aggregated data, we implement an accuracy metric and device quality score to exclude some noise. Some things we take into consideration:
There are some other known biases that would be hard to avoid, such as age bias when looking at usage during particular times of the day (waking/sleeping hours typically vary depending on age). There could also be a vacation bias that may increase activity when one is on vacation compared with regular daily activities. Another possible bias would be income bias where more affluent areas may have more devices (i.e., people from affluent areas may have more than 1 device).