Confronting models with data is only effective when the statistical model matches the biological one and the structure of your data collection is right for the statistical model. We outline some basic principles of sampling, emphasizing the importance of randomization. Randomization is also essential to experimental design, but so are controls, replication of experimental units, and independence of experimental units. This chapter emphasizes the distinction between sampling or experimental units representing independent instances and observational units representing things we measure or count from those units. Observational units may be subsamples of experimental units, but shouldn’t be confused with them. In this chapter, we also introduce methods for deciding how much data you need.
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