Repeated looks at accumulating data: to correct or not to correct?

Area of Science:

• Statistics
• Statistical Inference

Background:

• Sequential analysis offers an efficient method for decision-making in research by analyzing accumulating data.
• Traditional statistical methods often require fixed sample sizes, potentially leading to inefficient data collection.

Purpose of the Study:

• To compare and elucidate three distinct statistical approaches within the framework of sequential analysis.
• To highlight the less commonly discussed likelihood approach in sequential hypothesis testing.

Main Methods:

• Discussion of frequentist methods applied to sequential analysis.
• Exploration of Bayesian methodologies in the context of cumulative data analysis.
• Detailed examination of the likelihood approach for sequential hypothesis testing.

Main Results:

• The article provides a comparative overview of the frequentist, Bayesian, and likelihood approaches.
• It emphasizes the utility and mechanics of the likelihood approach in sequential settings.

Conclusions:

• Sequential analysis provides a flexible framework for statistical decision-making.
• The likelihood approach offers a valuable, though less recognized, alternative for sequential data analysis.