CM
● MODULE 04 / 20
Biostatistics, Sampling & Data Presentation
Describe data clearly, quantify uncertainty and avoid common errors in interpreting statistical tests.
Quick revision ↓ Medical Dictionary
FULL CHAPTER EXPLANATION
Deep Learning — Module 04
Connect population needs and measurements to prevention, practical action and evaluation.
01
Variable types and summaries
Nominal variables have categories without order; ordinal variables have ordered categories without assured equal spacing. Numerical variables may be discrete counts or continuous measurements. The mean uses every value and is sensitive to extremes; the median describes the middle ordered observation and is often useful for skewed data. The mode identifies a common value or category. Select a summary that fits the distribution and the clinical question.
02
Spread and precision
Range describes extremes, interquartile range the middle half of ordered data, and standard deviation variability around the mean. Standard error describes uncertainty of an estimator under the sampling model, not individual variability. For an independent sample mean it is estimated by standard deviation divided by the square root of sample size. Larger samples improve random precision but do not automatically correct measurement error or selection bias.
03
Inference and confidence intervals
A confidence interval communicates a range of parameter values compatible with the data and method at a chosen confidence level. In the usual frequentist interpretation, repeated intervals constructed by the method cover the fixed parameter at the stated long-run frequency. A p value describes compatibility of data with a specified null model, not the probability that the null is true. Report effect size, uncertainty and clinical relevance together.
04
Sampling and surveys
Simple random sampling gives eligible members a known selection chance. Stratification samples within defined subgroups; cluster sampling selects groups, often for practical fieldwork. Correlated observations within clusters affect precision and require design-aware analysis. A sampling frame that omits informal settlements cannot be repaired solely by a larger sample. Record nonresponse, weighting and eligibility, and distinguish representative selection from voluntary convenience recruitment.
05
Tables, tests and integrity
A bar chart suits categories and a histogram a numerical distribution; axes, denominators and units must be clear. Paired observations need methods that account for pairing. A chi-square test concerns categorical association with suitable assumptions; a t test concerns means with appropriate design and assumptions. Repeated unplanned testing raises false-positive risk. Prespecify key outcomes, protect participant privacy and retain a traceable correction history rather than editing inconvenient observations away.
High-Yield Revision
- Standard deviation describes spread; standard error describes precision.
- A p value is not the probability that a hypothesis is true.
- Sample size cannot repair a biased sampling frame.
Common Exam & Concept Pitfalls
- Do not equate statistical significance with clinical importance.
- Do not ignore clustering or pairing during analysis.
Sources & Further Reading
Curriculum and reference review: 24 September 2026. These original study notes summarize established principles. Use current programme schedules, local reporting requirements and clinical protocols for practice. Faculty review is pending.
- PM&DC MBBS Curriculum 2024 — Community Medicine and Public Health, printed pages 61–65
- Measurement and screening: CDC Principles of Epidemiology (archived foundational text) · WHO screening programme guide
- Services and surveillance: WHO primary health care · Pakistan NIH surveillance bulletins
- Immunization: Pakistan FDI official schedule · CDC timing and spacing principles. Apply Pakistan programme guidance to local schedules.
- Family health: WHO maternal health · Contraception · Infant feeding
- Nutrition: WHO malnutrition · Child anthropometric indices
- Environment and work: WHO drinking water · WASH · NIOSH hierarchy of controls
- Vectors and exposure: WHO dengue · Malaria prevention · Rabies · Snakebite
- Long-term health: WHO noncommunicable diseases · Mental health promotion and prevention
- Preparedness: WHO Health Emergency and Disaster Risk Management Framework
RAHBAR study structure: population need → measurement → prevention → action → evaluation. Use institutional curriculum and faculty guidance for local sequencing, practical requirements and assessment specifications.