Faculty of Engineering and Natural Sciences
Department of Genetics and Bioengineering

Code Name Level Year Semester
GBE 202 Biostatistics Undergraduate 2 Spring
Status Number of ECTS Credits Class Hours Per Week Total Hours Per Semester Language
Compulsory 4 2 + 2 90 English

Instructor Assistant Coordinator
Şenol Doğan, Assist. Prof. Dr. Senol Dogan Senol Dogan, Assist. Prof. Dr.
[email protected] [email protected] no email

Biostatistics is essential to ensuring that findings and practices in public health and biomedicine are supported by reliable evidence. This course covers the basic tools for the collection, analysis, and presentation of data in all areas of public health. Central to these skills is assessing the impact of chance and variability on the interpretation of research findings and subsequent recommendations for public health practice and policy. Topics covered include: general principles of study design; hypothesis testing; review of methods for comparison of discrete and continuous data including ANOVA, t-test, correlation, and regression.

Students who successfully complete this course will be able to:
•Describe the roles biostatistics serves in public health and biomedical research;
•Explain general principles of study design and its implications for valid inference when, for example, identifying risk factors for disease, isolating targets for prevention, and assessing the effectiveness of one or more interventions;
•Assess data sources and data quality for the purpose of selecting appropriate data for specific research questions;
•Translate research objectives into clear, testable statistical hypotheses;
•Describe basic principles and the practical importance of key concepts from probability and inference, inductive versus deductive reasoning, including random variation, systematic error, sampling error, measurement error, hypothesis testing, type I and type II errors, and confidence
•Apply numerical, tabular, and graphical descriptive techniques commonly used to characterize and summarize public health data;
•Identify appropriate statistical methods to be applied in a given research setting, apply these methods, and acknowledge the limitations of those methods;
•Evaluate computer output containing statistical procedures and graphics and interpret it in a public health context; and

Differentiate between quantitative problems that can be addressed with standard, commonly used statistical methods and those requiring input from a professional biostatistician.

  1. Introduction to Introduction to Biostatistics
  2. Descriptive Statistics and Graphical Displays
  3. Data and Variables
  4. Mean, Median. Quartiles
  5. Standard Deviations and Outliers
  6. Correlations, Pearson Correlation
  7. Midterm Exam

  1. Regression
  2. Inference Statistics, Two way tables
  3. Normal Distirbutions
  4. Z score, t-test
  5. Probability
  6. Chi-square
  7. ANOVA test
  8. Final Exam

  1. Introduction to Data
  2. Variables
  3. Filtering data
  4. Mean, Median. Quartiles calculations
  5. Standard deviation calculation
  6. Correlation calculations
  7. Midterm
  8. Regression questions
  9. Two tables comperison
  10. Z score calculation
  11. Drawing tables and normal distribution
  12. Probability
  13. Chi-square
  14. ANOVA
  15. Preparation for final

  • Interactive Lectures
  • Excersises
  • Presentation
  • Problem solving
Description (%)
Method Quantity Percentage (%)
Midterm Exam(s)130
Total: 30
Learning outcomes

      ECTS (Allocated based on student) WORKLOAD
      Activities Quantity Duration (Hour) Total Work Load
      Lecture (14 weeks x Lecture hours per week)14228
      Laboratory / Practice (14 weeks x Laboratory/Practice hours per week)14228
      Midterm Examination (1 week)122
      Final Examination(1 week)122
      Preparation for Midterm Examination11010
      Preparation for Final Examination12020
      Assignment / Homework/ Project 0
      Seminar / Presentation 0
      Total Workload: 90
      ECTS Credit (Total workload/25): 4