STAT 109 Biostatistics Study Notes

Foundations and Scope of Biostatistics

  • Academic and Institutional Context:

    • Institution: King Saud University, College of Sciences, Department of Statistics & Operations Research.
    • Course Code & Title: STAT 109 Biostatistics.
    • Document Date: September 2025.
    • Authorship & Revision History: Class notes originally authored by Professor Abdullah al-Shiha; subsequently revised and updated by Dr. Razan Alsehibani, Sana AbuNasrah, Eidah Al-Enazi, Zahra M Kaabi, Kholoud Basalim, and Maram Al-Shammari.

  • Curriculum Outline (Course Roadmap):

    • Chapter 1: Getting Acquainted with Biostatistics
    • Chapter 2: Strategies for Understanding the Meaning of Data
    • Chapter 3: Probability as the Basis of Statistical Inference
    • Chapter 4: Probabilistic Features of Certain Data Distribution (Probability Distributions)
    • Chapter 5: Probabilistic Features of the Distributions of Certain Sample Statistics
    • Chapter 6: Using Sample Data to Make Estimations about Population Parameters
    • Chapter 7: Using Sample Statistics to Test Hypotheses about Population Parameters

  • Two Core Branches of Statistics:

    • Descriptive Statistics: Techniques used to organize, summarize, and describe data clearly and effectively.
    • Inferential Statistics: Techniques that enable decision-making, estimation, and drawing conclusions about an entire target population by analyzing only a smaller, representative subset of that population.

  • Definition of Biostatistics:

    • The specialized branch of statistics applied when data are derived from medicine, epidemiology, public health, and the biological sciences.

  • Primary Data Sources:

    1. Routinely Kept Records: Pre-existing administrative or health records, such as medical charts, disease registries, and institutional logs.
    2. Surveys: Data systematically collected from target groups via questionnaires, interviews, or observations.
    3. Experiments: Data generated under controlled scientific conditions where specific variables are manipulated to observe outcomes.
    4. External Sources: Secondary data obtained from published reports, open data banks, national statistical repositories, or research literature.

Population, Sample, and Variable Concepts

  • Population Fundamentals:

    • Definition: A population represents the complete and largest collection of entities (individuals, patients, biological items, or elements) of interest at a specific point in time, about which conclusions are sought.
    • Population of Values: Created when a specific characteristic or measurement is taken across every individual entity comprising the population.
    • Population Size Symbol: Denoted by the uppercase letter NN, representing the exact total number of elements in the full population.

  • Sample Fundamentals:

    • Definition: A sample is a subset or portion of elements selected directly from the population.
    • Sample Size Symbol: Denoted by the lowercase letter nn, representing the total number of observed elements included in the sample.

  • Illustrative Case Study (King Saud University Student Weight Study):

    • Objective: Study weight characteristics among engineering students enrolled at King Saud University (KSU).
    • Procedure: A group of 5050 students is randomly selected from the College of Engineering at KSU, and their individual weights are recorded.
    • Population (NN): All students enrolled in the College of Engineering at KSU.
    • Sample (nn): The selected 5050 students (n=50n = 50).
    • Variable: Weight of the individual students.

Classification and Taxonomy of Variables

  • General Definition of a Variable:

    • A characteristic or property measured on individual elements within a study. Its values naturally vary from one element to another (e.g., height, blood sugar level, grade, sex).

  • Quantitative Variables:

    • Characteristics whose values are numerical quantities representing