A systematic error in study design or data collection that occurs when the participants included in a study are not representative of the population the study intends to describe. Selection bias can inflate or deflate treatment effects, making results unreliable for broader application.

In the peptide context, selection bias is pervasive in community reports. People who post about their positive experiences with BPC-157 on Reddit are not a random sample—those who saw no effect or experienced problems are less likely to post. Similarly, clinical trials that use strict inclusion/exclusion criteria may select healthier, more motivated patients whose outcomes don’t reflect what happens in general use. Recognizing selection bias is essential for honestly evaluating the gap between community enthusiasm and clinical evidence.