A Primer on Large-Sample Statistical Inference for Epidemiologists

Area of Science:

• Epidemiology
• Biostatistics
• Public Health

Background:

• Statistical theory is crucial for epidemiological research and advanced methods like causal inference.
• Existing textbooks offer comprehensive but potentially overwhelming coverage of statistical theory.
• A focused summary of large-sample statistical theory is needed for health science researchers.

Purpose of the Study:

• To provide a focused summary of fundamental large-sample statistical theory concepts.
• To enhance the understanding and application of statistical methods in epidemiologic research.
• To clarify commonly confused statistical topics relevant to health science.

Main Methods:

• Summarization of fundamental concepts from large-sample statistical theory.
• Explanation of assumptions underlying common statistical methods for valid inference.
• Contextualization with a real-world example from the Women's Interagency HIV Study.

Main Results:

• A concise overview of essential large-sample statistical theory is presented.
• Key statistical topics often misunderstood by researchers are clarified.
• The importance of statistical assumptions for valid inference is highlighted.

Conclusions:

• This primer facilitates a more focused understanding of statistical theory for epidemiologists and health researchers.
• Improved understanding promotes appropriate application of statistical methods in population health and medical studies.
• The content aims to bridge the gap between theoretical statistics and practical epidemiologic research.