Sampling inference, an alternate statistical model

Area of Science:

• Statistics
• Data Science
• Research Methodology

Background:

• Classical and Bayesian inference methods have limitations.
• A need exists for more robust inference paradigms in real-world scenarios.
• Existing sampling methods may not always yield valid results.

Purpose of the Study:

• To introduce sampling inference as an alternative to traditional statistical paradigms.
• To evaluate sampling inference against classical, Bayesian, and probabilistic systems.
• To propose a probabilistic real-world system for enhanced inference.

Main Methods:

• Discussed sampling inference in the context of classical and Bayesian approaches.
• Proposed a probabilistic real-world system for statistical modeling.
• Applied sampling inference to probabilistic models for analyzing different sample types.

Main Results:

• Sampling inference provides a valid approach for volunteer, representative, and random samples.
• A probabilistic real-world system is deemed more valid and practical.
• Introduced novel concepts of time and distance-based sampling.

Conclusions:

• Sampling inference is a valuable alternative paradigm for statistical analysis.
• Probabilistic models integrated with sampling inference offer superior utility.
• The proposed framework supports robust inferences in diverse research contexts.