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Attribute data generally require probability samples; cultural data do not, because the shared and socially constructed nature of cultural phenomena violates the assumption of case independence in classical sampling theory (Gravlee 2005, p. 953). This assumption is often warranted with attribute data – your age is unrelated to mine. But if we participate in the same culture, your understanding of what it means to get older and mine are bound together, because people acquire cultural knowledge through social interaction. Thus, efficient ethnographic samples should select units of analysis to represent the range of variability in life experiences and social contexts related to the transmission of culture (Guest 2015; Johnson 1990). Probability samples are not necessary for achieving this aim, as probability and nonprobability samples have been shown to yield identical conclusions about cultural data (Handwerker and Wozniak 1997).

There are many options for probability and nonprobability sampling designs. Miles and Huberman (1994, p. 28) list 16 types of nonprobability sampling. Onwuegbuzie and Leech (2007) identify 24 sampling designs, and Teddlie and Tashakkori (2009, p. 170) delineate 26, including a mix of probability and nonprobability methods. These complex typologies build on a small set of basic sampling designs summarized in ssss1. For details about probability and nonprobability sampling in anthropology, see Bernard (2018), Guest (2015), Johnson (1990), and Schensul and LeCompte (2013, pp. 280–318).