On the Sampling Size for Inverse Sampling

Daniele Cuntrera, Vincenzo Falco () and Ornella Giambalvo
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Daniele Cuntrera: Department of Business, Economics, and Statistics, University of Palermo, Viale delle Scienze, Building 13, 90128 Palermo, Sicily, Italy
Vincenzo Falco: Department of Business, Economics, and Statistics, University of Palermo, Viale delle Scienze, Building 13, 90128 Palermo, Sicily, Italy
Ornella Giambalvo: Department of Business, Economics, and Statistics, University of Palermo, Viale delle Scienze, Building 13, 90128 Palermo, Sicily, Italy

Stats, 2022, vol. 5, issue 4, 1-15

Abstract: In the Big Data era, sampling remains a central theme. This paper investigates the characteristics of inverse sampling on two different datasets (real and simulated) to determine when big data become too small for inverse sampling to be used and to examine the impact of the sampling rate of the subsamples. We find that the method, using the appropriate subsample size for both the mean and proportion parameters, performs well with a smaller dataset than big data through the simulation study and real-data application. Different settings related to the selection bias severity are considered during the simulation study and real application.

Keywords: big data; sampling statistics; inverse sampling (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2022
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