Can coders of responses generated by open-ended survey questions consistently capture the meaning provided by respondents to those surveys? This is the question of reliability and validity. Researchers who use content analysis typically think that coder inference is a threat to validity, that is, when coders are allowed to make inferences instead of automatically following a series of coding rules, those coders will introduce a wide variety of variation of meaning, thus preventing researchers from making strong claims for reliability and validity. But is that always the case? In this presentation I argue that coder inference is not only allowable in certain situations, it is required and highly desirable. My argument begins with an examination of the standards we use to make our evaluative judgments of reliability and validity. Then I challenge some of the assumptions we make when designing coding rules, when training coders, and when assessing reliability and validity.