Data
Official data in SubjectManager for the following academic year: 20242025
Course director

Borbásné Farkas Kornélia
associate professor,
Institute of Bioanalysis 
Number of hours/semester
lectures: 14 hours
practices: 14 hours
seminars: 0 hours
total of: 28 hours
Subject data
 Code of subject: OSAFI1T
 2 kredit
 Dentistry
 Basic modul
 spring

Course headcount limitations
min. 5 – max. 200
Topic
The aim of the course is: reviewing and processing data and characterization using graphical and numerical tools, probability and statistical conclusion/decision, basic statistical methods commonly used in medicine and medical practice.
The other goal is introducing and practising the basics of statistical thinking. The skills and competences can be developed with statistics in scientific background and daily work of medicine. Analytical and critical thinking are of high importance in medical decision making, and there is also an opportunity to develop these skills within the subject.
Lectures
 1. Introduction (Biometrics and Medical Sciences). Probability and relative frequency.  Kőnigné Péter Anikó
 2. Variables and its representation.  Kőnigné Péter Anikó
 3. Probability calculation and discrete distributions.  Dergez Tímea
 4. Description statistics.  Dergez Tímea
 5. Continuous probability distributionsnormal distribution.  Kőnigné Péter Anikó
 6. Statistical estimations. Confidence interval for the expected value.  Dergez Tímea
 7. Principle of hypothesis testing. The one sample and the paired samples t tests.  Kőnigné Péter Anikó
 8. The confidence interval and the hypothesis testing. Type I and type II errors.  Dergez Tímea
 9. The independent samples t test. The power of the test.  Kőnigné Péter Anikó
 10. The nonparametric tests: Sign, Wilcoxon, MannWhitney tests.  Dergez Tímea
 11. Linear regression and correlation.  Dergez Tímea
 12. The evaluation of frequency data: Chisquared test and Fisher's exact test. Special applications.  Kőnigné Péter Anikó
 13. The principle of the ANOVA.  Dergez Tímea
 14. Summary of the hypothesis testing methods.  Kőnigné Péter Anikó
Practices
 1. Relative frequency and probability, thinking models (deterministic and stochastic models).
 2. Types of data and their representation.
 3. Probability calculationBinomial and Poisson distribution.
 4. Exploring data by numbers  descriptive statistics
 5. Normal distribution. The distribution of the sample mean.
 6. Estimations. The confidence interval of the expected value.
 7. 1st midterm test. The hypothesis testing  the one sample (and the paired samples) t tests.
 8. Two independent samples t test. The Type one and Type two errors.
 9. Nonparametric tests.
 10. The linear regression and correlation.
 11. Contingency tables  the chisquares test, Fisher's exact test.
 12. Summary.
 13. Practice.
 14. 2nd midterm test.
Seminars
Reading material
Obligatory literature
Literature developed by the Department
Moodle, PotePedia files
Notes
Moodle electronic notes
Joseph Belágyi: Medical Biometry
Sára Jeges: Biometry
L. Pótó: Biometrics. Workbook for the Practices, Pécs, 2020
Recommended literature
1. D.S. Moore, G.P. McCabe: Introduction to the Practice of Statistics, 5th ed., W.H. Freeman 2005
2. D. Yates, D.S. Moore, D.S. Starnes: The Practice of Statistics (TI83/89 Graphing Calculator Enhanced) 2/e, 2003, W.H. Freeman
3. W.G. Rees: Essential Statistics, Chapman and Hall, 1992
Conditions for acceptance of the semester
There are two midterm tests during the semester with at least 60% result and short tests at the beginning of all practices. Only three absences are allowed.
Exam: a test on an electronic platform with a minimum score of 60% or an oral test with a computerbased problem and a theoretical question. In each of these parts should be completed at least satisfactory for a successful exam.
Midterm exams
There are two midterm tests during the semester with at least 60% result and short tests at the beginning of all practices. Only three absences are allowed.
Making up for missed classes
Retake class.
Exam topics/questions
Themes of theoretical part:
1. The main goals / potential results of learning biometrics/biostatistics.
2. The key feature of the statistical thinking  The probability.
3. The idea of the probability distribution  discrete distributions.
4. The basic principles of statistical thinking  from the data to the decision: size of the sample, representativity, lurking variables.
5. Types of the data (variables) and displaying them with graphs.
6. Graphical and numerical characterization of the sample and the population
7. Numerical description of continuous data: five number and three number descriptions.
8. The idea of the probability distribution  continuous distributions.
9. The normal distribution, the central limit theorem.
10. Statistical estimation: point estimation and interval estimation.
11. The confidence interval of the population mean
12. The basic idea of hypothesis testing
13. The one sample and the paired ttest
14. Comparing the confidence interval and the hypothesis testing.
15. The risk of errors and the power of a test.
16. The two (independent) samples ttest.
17. Connection between two variables  in case of continuous variables.
18. Connection between two variables  in case of categorical variables.
19. Special application of the contingency table, qualification of a diagnostic test (sensitivity, specificity, predictive values).
20. Nonparametric tests in case of one, paired sample
21. Nonparametric tests in case of independent samples.
22. The principle of the Analysis of Variance (ANOVA).
Examiners
 Dergez Tímea
 Kőnigné Péter Anikó
 Makszin Lilla
Instructor / tutor of practices and seminars
 Dergez Tímea
 Kőnigné Péter Anikó
 Makszin Lilla