Lecture 1

Course Information

  • Course Title: STAT110: BIOSTATISTICS I

  • Instructor: Asst. Prof. Dr. Zalihe YARKINER

  • Email: zyarkiner@ciu.edu.tr

  • Office Location: Room GE229

  • Extension: 2641

Reference Text Book

  • Title: Biostatistics: A Foundation for Analysis in the Health Sciences, 9th Edition

  • Author: Wane W. Daniel

Lecture One: Introduction to Biostatistics

  • Definition: Statistics is concerned with the collection, organization, summarization, and analysis of data.

    • Types of Statistics:

      • Discrete Statistics: Collection and analysis of data.

      • Inferential Statistics: Drawing inferences about a larger body of data based on a sample.

Importance of Statistics

  • Essential for the treatment of numerical data derived from groups.

  • Helps in interpreting and communicating results affected by various causes.

  • The information age: 0.5 million new medical articles published annually.

Data Analysis Needs

  • Importance of knowing how to obtain, analyze, and interpret data.

  • Data is available in numerical form (values).

Biostatistics Overview

  • Field focusing on data derived from biological sciences and medicine.

Types of Statistics

  • Descriptive Statistics: Collection, organization, presentation, and summarization of data.

  • Inferential (Analytical) Statistics: Decision-making about a large dataset based on a smaller sample.

Definitions

  • Data (Datum): The raw material of statistics, obtained from measurements or counting.

  • Value: Numerical representation of the measurement of a variable.

Motivation for Statistical Analysis

  • Driven by the need to answer specific questions requiring suitable data sources:

    1. Routine records (e.g., hospital records)

    2. Surveys (for unavailable data)

    3. Experiments

    4. External sources (published reports, data banks)

Variables

  • Definition: Characteristics that can take different values; examples include height, weight, and age.

  • Types of Variables:

    • Quantitative Variables: Measured in numerical units (e.g., height and weight).

    • Qualitative Variables: Categorically assessed (e.g., sex, ethnicity).

Classifications of Quantitative Variables

  • Discrete Quantitative Variables: Gaps or interruptions in values (e.g., hospital admissions).

  • Continuous Quantitative Variables: No gaps; can take any value within a range (e.g., height and weight).

Measurement Scales

  • Nominal Scale: Categories without intrinsic order (e.g., male/female).

  • Ordinal Scale: Categories with a defined order (e.g., high/low).

  • Interval Scale: Numeric scale without a true zero (e.g., age intervals).

  • Ratio Scale: Numeric scale with a true zero (e.g., height).

Populations and Samples

  • Population: The complete set of entities sharing at least one characteristic of interest.

  • Sample: A representative subset of the population, chosen via sampling methods (random or non-random).

Upcoming Lectures

  • Next Topics: Summarization and presentation of data in Lectures Two & Three.