BIO512 Biostatistics
Syllabus | International University of Sarajevo - Last Update on Mar 03, 2026
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Genetics and Bioengineering
Muhamed Adilović
Course Lecturer
Course Objectives
After completing this course, you will (1) have a deeper appreciation for how to interpret and look at data; (2) understand how statistics and probability apply to real-world problems; and (3) be able to critically evaluate the statistics in medical studies.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
There is no textbook as such. Instead we will use slides and weekly reading assignments (papers from medical journals, newspapers etc.). One form of reference will be "what is the P-value anyway? - 34 stories to help you actually understand statistics Andrew Vickers", Addison Wesley 1st Ed. 2010.
Additional Literature
Teaching Methods
Course slides involving discussions with examples
Lectures are divided in teaching theoretical concepts and applying these in practice using R and real world data sets
Discussion about weekly reading assignment involving statistical parts of scientific literature from the particular area of students specific application area
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Course introduction; Introduction to statistics; | Course slides |
| 2 | Data presentation and visualization techniques; Population and Sample; | Course slides |
| 3 | Measures of central tendency of the data; Shape of the distribution; Variability in the data; | Course slides |
| 4 | Measures of variability (range, IQR, variance, SD); Normal distribution; | Course slides |
| 5 | Standard normal distribution and Z-scores; Reading standard normal table and using statistical software for the same purpose; | Course slides |
| 6 | Sampling distribution; Sampling distribution of the sample mean; | Course slides |
| 7 | Standard error of the mean; Central Limit Theorem; | Course slides |
| 8 | Interval estimation around the mean; Review of study designs; (Quiz 1) | Course slides |
| 9 | Measures of disease risk and association; | Course slides |
| 10 | Statistical inference (confidence intervals and hypothesis testing); | Course slides |
| 11 | Statistical hypothesis testing; mean, proportion (one-, two sample, variance known, variance unknown). | Course slides |
| 12 | P-value pitfalls; types I and type II error; statistical power; overview of statistical tests | Course slides |
| 13 | P-value pitfalls; types I and type II error; statistical power; overview of statistical tests. | Course slides |
| 14 | Regression analysis; linear correlation and regression. (Quiz 2) | Course slides |
| 15 | Discussion of student specific data project (Final data analysis report) | Course slides |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| BIO512.1 | Course | Wednesday 10:00 - 12:50 | A F1.18 - Computer Lab | - | - |
Office Hours & Room
| Day | Time | Office | Notes |
|---|---|---|---|
| Tuesday | 08:00 - 11:00 | A F1.33 | |
| Wednesday | 08:00 - 11:00 | A F1.33 |
Assessment Methods and Criteria
Assessment Components
Final Project report
AI: Not AllowedAlignment with Learning Outcomes : LO 1 2 5
Quizz
AI: Not AllowedAlignment with Learning Outcomes : LO 3 4
IUS Grading System
| Grading Scale | IUS Grading System | IUS Coeff. | Letter (B&H) | Numerical (B&H) |
|---|---|---|---|---|
| 0 - 44 | F | 0 | F | 5 |
| 45 - 54 | E | 1 | ||
| 55 - 64 | C | 2 | E | 6 |
| 65 - 69 | C+ | 2.3 | D | 7 |
| 70 -74 | B- | 2.7 | ||
| 75 - 79 | B | 3 | C | 8 |
| 80 - 84 | B+ | 3.3 | ||
| 85 - 94 | A- | 3.7 | B | 9 |
| 95 - 100 | A | 4 | A | 10 |
Late Work Policy
Information about late submission policies will be shared during class and posted in this section. Please check back for official guidelines.
ECTS Credit Calculation
📚 Student Workload
This 6 ECTS credit course corresponds to 150 hours of total student workload, distributed as follows:
Lecture hours
45 hours ⏳ (15 week × 3 h)
Home study
45 hours ⏳ (15 week × 3 h)
In-term exam study
40 hours ⏳ (2 week × 20 h)
Final report study
20 hours ⏳ (1 week × 20 h)
150 Total Workload Hours
6 ECTS Credits
Course Policies
Academic Integrity
All work submitted must be your own. Plagiarism, cheating, or any form of academic dishonesty will result in disciplinary action according to university policies. When in doubt about citation practices, consult the instructor.
Attendance Policy
Students are expected to adhere to the attendance requirements as outlined in the International University of Sarajevo Study Rules and Regulations. Excessive absences, whether excused or unexcused, may impact academic performance and eligibility for assessment. Mandatory sessions (e.g., labs, workshops) require attendance unless formally exempted. For detailed policies on absences, documentation, and penalties, please refer to the official university regulations.
Technology & AI Policy
Laptops/tablets may be used for note-taking only during lectures. Phones should be silenced and put away during all class sessions. Audio/video recording requires prior permission from the instructor.
Artificial Intelligence (AI) Usage: The use of AI tools (e.g., ChatGPT, Copilot, Gemini) varies by assessment component. Please refer to the AI usage indicator next to each assessment item in the Assessment Methods and Criteria section above. Submitting AI-generated content as your own work, where AI is not explicitly allowed, constitutes an academic integrity violation.
Communication Policy
All course-related communication should occur through official university channels (institutional email or SIS). Emails should include [BIO512] in the subject line.
Academic Quality Assurance Policy
Course Academic Quality Assurance is achieved through Semester Student Survey. At the end of each academic year, the institution of higher education is obliged to evaluate work of the academic staff, or the success of realization of the curricula.
Learning Tips
Be prepared to contribute thoughtfully during class discussions, labs, or collaborative work. Active participation deepens understanding and encourages critical thinking.
Complete assigned readings or prep materials before class. Take notes, highlight key ideas, and jot down questions. Aim to grasp core concepts and their applications—not just facts.
Use course frameworks or methodologies to analyze problems, case studies, or projects. Begin early to allow time for reflection and refinement. Seek feedback to improve your work.
Don’t hesitate to reach out when something is unclear. Use office hours, discussion boards, or peer networks to clarify concepts and stay on track.
Syllabus Last Updated on Mar 03, 2026 | International University of Sarajevo
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Referencing Curricula Print this page
| Course Code | Course Title | Weekly Hours* | ECTS | Weekly Class Schedule | ||||||
| T | P | |||||||||
| BIO512 | Biostatistics | 3 | 0 | 6 | ||||||
| Prerequisite | None | It is a prerequisite to | - | |||||||
| Lecturer | Muhamed Adilović | Office Hours / Room / Phone | Tuesday: 8:00-11:00 Wednesday: 8:00-11:00 |
|||||||
| madilovic@ius.edu.ba | ||||||||||
| Assistant | Assistant E-mail | |||||||||
| Course Objectives | After completing this course, you will (1) have a deeper appreciation for how to interpret and look at data; (2) understand how statistics and probability apply to real-world problems; and (3) be able to critically evaluate the statistics in medical studies. | |||||||||
| Textbook | There is no textbook as such. Instead we will use slides and weekly reading assignments (papers from medical journals, newspapers etc.). One form of reference will be "what is the P-value anyway? - 34 stories to help you actually understand statistics Andrew Vickers", Addison Wesley 1st Ed. 2010. | |||||||||
| Additional Literature | ||||||||||
| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
| Teaching Methods | Course slides involving discussions with examples. Lectures are divided in teaching theoretical concepts and applying these in practice using R and real world data sets. Discussion about weekly reading assignment involving statistical parts of scientific literature from the particular area of students specific application area. | |||||||||
| Teaching Method Delivery | Teaching Method Delivery Notes | |||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Course introduction; Introduction to statistics; | Course slides | ||||||||
| Week 2 | Data presentation and visualization techniques; Population and Sample; | Course slides | ||||||||
| Week 3 | Measures of central tendency of the data; Shape of the distribution; Variability in the data; | Course slides | ||||||||
| Week 4 | Measures of variability (range, IQR, variance, SD); Normal distribution; | Course slides | ||||||||
| Week 5 | Standard normal distribution and Z-scores; Reading standard normal table and using statistical software for the same purpose; | Course slides | ||||||||
| Week 6 | Sampling distribution; Sampling distribution of the sample mean; | Course slides | ||||||||
| Week 7 | Standard error of the mean; Central Limit Theorem; | Course slides | ||||||||
| Week 8 | Interval estimation around the mean; Review of study designs; (Quiz 1) | Course slides | ||||||||
| Week 9 | Measures of disease risk and association; | Course slides | ||||||||
| Week 10 | Statistical inference (confidence intervals and hypothesis testing); | Course slides | ||||||||
| Week 11 | Statistical hypothesis testing; mean, proportion (one-, two sample, variance known, variance unknown). | Course slides | ||||||||
| Week 12 | P-value pitfalls; types I and type II error; statistical power; overview of statistical tests | Course slides | ||||||||
| Week 13 | P-value pitfalls; types I and type II error; statistical power; overview of statistical tests. | Course slides | ||||||||
| Week 14 | Regression analysis; linear correlation and regression. (Quiz 2) | Course slides | ||||||||
| Week 15 | Discussion of student specific data project (Final data analysis report) | Course slides | ||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Project report | 1 | 40 | LO 1, 2, 5 | Not Allowed | |
| Semester Evaluation Components | |||||
| Quizz | 2 | 60 | LO 3,4 | Not Allowed | |
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture hours | 3 | 15 | 45 | Home study | 3 | 15 | 45 | |||
| In-term exam study | 20 | 2 | 40 | Final report study | 20 | 1 | 20 | |||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 27/03/2026 | |||||||||