ABSTRACT
Although there are many factors in that process that are important for assuring quality data, this chapter focuses on two of the most critical: statistical inference and non-sampling errors. Statistical inference determines whether, and to what extent, the results from the study can be generalized to a broader set of farming operations. An examination of non-sampling errors will show where things are likely to go wrong in a data collection process and how those errors can affect the resulting quality. To illustrate these concepts with concrete examples, the chapter explores common approaches to collecting cost and returns data and compare their relative potential to produce quality data. Common examples of target populations for cost of production studies could be all farms, all farms producing corn in a certain geographic area, or all large progressive farms growing corn in that same area. The chapter concludes by examining the tradeoffs that are often needed between affordability and the other aspects of quality.
