On the Sampling Size for Inverse Sampling

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