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Methodology minute: A statistical test primer for infection prevention and control.
Timothy L Wiemken1, Samson L Niemotka2, Christopher Prener3
1Saint Louis University School of Medicine Department of Internal Medicine, Division of Infectious Diseases, Allergy and Immunology, Saint Louis, MO; Saint Louis University Systems, Infection Prevention Center, Saint Louis, MO; Saint Louis University Institute for Vaccine Science and Policy, Data Science and Epidemiology Core, Saint Louis, MO.
American Journal of Infection Control
|April 19, 2021
Summary
This primer guides infection preventionists (IPs) in selecting appropriate statistical tests for data analysis and literature evaluation. Understanding hypothesis testing is crucial for accurate interpretation of research findings.
Area of Science:
- Epidemiology
- Biostatistics
- Infection Control
Background:
- Statistical test selection is a vital skill for infection preventionists (IPs).
- Evaluating scientific literature requires understanding data analysis methodologies.
- Computational advancements simplify data analysis but not interpretation.
Purpose of the Study:
- To introduce infection preventionists to hypothesis testing concepts.
- To focus on the selection process for appropriate statistical tests.
- To enhance the critical appraisal of research in infection prevention.
Main Methods:
- This primer focuses on the conceptual understanding of hypothesis testing.
- It emphasizes the practical aspects of choosing the correct statistical test.
- No specific datasets or experiments were analyzed; it is a conceptual guide.
Main Results:
- The study provides a foundational understanding of statistical principles for IPs.
- It highlights the importance of sound methodology in data interpretation.
- Key considerations for selecting statistical tests are outlined.
Conclusions:
- Appropriate statistical test selection is fundamental for infection prevention research.
- A solid grasp of hypothesis testing improves the quality of data analysis and interpretation.
- This primer serves as a resource for IPs to build statistical competence.