  We study how a decision maker (DM) learns about the biases of unfamiliar
information sources. Absent any friction, a rational DM uses familiar sources
as yardsticks to discern the true bias of a source. If the DM has misspecified
beliefs, this process fails. We derive long-run beliefs, behavior, welfare, and
corresponding comparative statics when the DM holds dogmatic, incorrect beliefs
about the biases of several familiar sources. The distortion due to
misspecified learning aggregates misspecifications associated with familiar
sources independently of sources the DM learns about. This has implications for
labor market discrimination, media bias, and project finance and oversight.
