Which of the following is NOT a type of error in statistical analysis?

The focus of the question is on identifying what constitutes a type of error specifically in the context of statistical analysis. Sampling error refers to the discrepancy that arises when a sample does not perfectly represent the population from which it is drawn. This is a recognized source of error in statistics that affects the reliability of inferences made from sample data.

Data entry error refers to mistakes made when inputting data into a database or system, which can lead to inaccurate results in analysis. This is another type of error that can affect statistical outcomes by introducing inaccuracies in the data used for analysis.

Calculation error involves mistakes made during the computation process, such as miscalculating averages or standard deviations. This type of error can significantly alter the results and conclusions drawn from data analysis.

On the other hand, "Error" on its own is a broad term that does not specify a particular type of error relevant to statistical analysis. It acts as a general category for various mistakes but does not fit as a specific type of error like the others listed. Thus, while sampling error, data entry error, and calculation error are all recognized categories of errors in statistical analysis, "Error" does not denote a specific type and therefore is not relevant in the context of the options provided.