Topic 1 · 22.1–22.1.1 · Free
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Chapter 22 — Chapter 22 — Biostatistics
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📘Easy summary
Biostatistics applies statistical methods to biological, medical and health-related data: collection, presentation, analysis and interpretation of data to draw meaningful conclusions. It lets researchers make decisions about populations from sample data instead of testing entire populations (impractical or costly), e.g. estimating prevalence of a condition among all workers from a representative subset. Components: 1. Study design and experimental planning (question, objectives, population, variables, randomization, confounding factors, sample size). 2. Data collection — primary (experiments, surveys, interviews, observations, clinical trials) and secondary (published research, medical records, government reports, databases). 3. Data management and processing (cleaning, coding, storage, confidentiality). 4. Data analysis — descriptive statistics (mean, median, mode, SD) and inferential statistics (hypothesis testing, regression, modelling). 5. Interpretation of results. 6. Presentation and communication (charts, graphs, clear language).
⭐Key points
- Sample → population conclusions.
- Primary vs secondary data.
- Descriptive vs inferential statistics.
🔬Diagram
🌿Real-life example
A survey of 500 workers estimates diabetes prevalence among all factory workers.
💡Exam tip
- Experiments and surveys = primary data.
✅Quick check
1. Primary data source:
2. Hypothesis testing belongs to:
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