Why stratification may hurt, & how much
Chris A.J. Klaassen, Andries J. Lenstra
TL;DR
This paper investigates conditions where stratified sampling can be less effective than simple random sampling, providing bounds and explanations for this counterintuitive phenomenon.
Contribution
It offers a detailed analysis of when and why stratification may be detrimental, including bounds and theoretical insights into its potential drawbacks.
Findings
Stratification can sometimes worsen sampling efficiency.
Bounds for the extent of stratification's negative impact are provided.
An explanation for the conditions leading to stratification's failure is given.
Abstract
There are circumstances under which stratified sampling is worse than simple random sampling, even if the allocation of the sample sizes is optimal. This phenomenon was discovered more than sixty years ago, but is not as widely known as one might expect. We provide it with lower and upper bounds for its badness as well as with an explanation.
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Taxonomy
TopicsAdvanced Statistical Process Monitoring · Advanced Statistical Methods and Models · Machine Learning and Algorithms