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Increasingly, the mental health needs of populations are measured using large-sample surveys with standardized measures and methods. Such efforts, however, rarely include sufficient number of smaller, culturally defined populations to draw defensible conclusions about their needs. Furthermore, without some adaptation, the standardized methods and measures may yield invalid results in such populations. Using a recently completed psychiatric epidemiology and services study with American Indian populations as a case example, this paper outlines issues facing epidemiologists working in such culturally diverse contexts. The issues discussed include the following: (1) persuading the scientific community and potential sponsors that work with distinct or culturally defined populations is important; (2) framing research questions and activities to meet the needs of communities; (3) defining a population of inference; (4) balancing the needs for comparability and cultural specificity; (5) maximizing scientific validity in light of the challenges in sample acquisition; and (6) developing and implementing data collection methods that uphold scientific standards but are also realistic given the context. The authors draw on their experiences--most recently in the American Indian Service Utilization, Psychiatric Epidemiology, Risk and Protective Factors Project (AI-SUPERPFP)--to illustrate these issues and suggest ways to address each. A goal of this paper is to challenge those invested in conducting culturally valid epidemiologic work in such populations to better articulate the nature of these efforts. [ABSTRACT FROM AUTHOR]