Sampling bias (QA) is a systematic distortion that occurs when the set of calls selected for quality review is not representative of the full population of customer interactions. It often shows up when reviews over-focus on certain agents, queues, call types, time periods, or only the easiest or most problematic calls.
Operationally, sampling bias matters because it can misstate true performance and risk. Leaders may think compliance, empathy, or resolution rates are better (or worse) than they actually are, which leads to misdirected coaching, inaccurate scorecards, and missed process issues that are happening in the unreviewed majority of calls.
It can also create fairness problems: agents who get sampled more heavily may appear to perform worse simply due to higher scrutiny, while others avoid review. Over time, biased sampling reduces trust in QA and makes it harder to prioritize training, staffing, and policy changes based on reliable evidence.
