When analysing data, the two kinds of error to watch for are nonsampling errors and:

1. Not every error in a study comes from the act of sampling. 2. When you analyze data, it is important to be aware of sampling errors and nonsampling errors. 3. Sampling error comes from the fact that a sample is only part of the population. 4. Nonsampling error covers everything else, such as a badly worded question or a faulty instrument. 5. Rounding and arithmetic slips are ordinary mistakes rather than these two named categories. _Source: OpenStax Introductory Statistics 2e (CC BY 4.0), Ch 1 'Sampling and Data', sections 1.1-1.4_