Introduction to statistical methods in biology WB-BI-ANG-68
The aim of the course is to familiarize students with basic methods of data analysis and to develop their skills in conducting analyses and interpreting their results. Participants will become acquainted with statistical software, calculating measures of central tendency and variability, formulating statistical hypotheses, conducting parametric and non-parametric tests for one and two samples (independent and dependent). Additionally, they will acquire the ability to perform analysis of variance and post hoc tests for multiple samples, correlation analysis, and linear regression analysis.
(in Polish) Grupa przedmiotów ogólnouczenianych
(in Polish) Opis nakładu pracy studenta w ECTS
Subject level
Learning outcome code/codes
Type of subject
Preliminary Requirements
Course coordinators
Learning outcomes
Subject Outcome 1: The graduate is familiar with basic methods of data analysis and understands the significance of statistical analyses in interpreting biological phenomena.
Subject Outcome 2: The graduate is able to apply basic methods of statistical analysis and interpret their results.
Subject Outcome 3: The graduate can formulate and test statistical hypotheses.
Subject Outcome 4: The graduate is prepared to prioritize tasks in task execution.
Assessment criteria
Grading criteria:
Subject Outcome 1-4 - final exam (practical data analysis tasks)
The final grade will be based on the classification provided below:
94 – 100% - 5.0
88 – 93% - 4.5
80 – 87% – 4.0
70 – 79% – 3.5
60 – 69% – 3.0
< 59.9% - 2.0
Grading criteria:
Knowledge:
For a grade of 2.0 (unsatisfactory): The student does not know the basic methods of data analysis and does not understand them.
For a grade of 3.0 (satisfactory): The student has mastered the knowledge and understanding of basic methods of data analysis to a sufficient extent.
For a grade of 4.0 (good): The student has a good understanding of basic methods of data analysis but struggles with formulating hypotheses and interpreting results.
For a grade of 5.0 (excellent): The student demonstrates a very good understanding of data analysis methods, is adept at formulating hypotheses, and interpreting results.
Skills:
For a grade of 2.0 (unsatisfactory): The student cannot apply basic statistical methods, formulate statistical hypotheses, and interpret analysis results.
For a grade of 3.0 (satisfactory): The student has sufficiently mastered the application of statistical methods in data analysis, formulates statistical hypotheses adequately, and interprets analysis results satisfactorily.
For a grade of 4.0 (good): The student has a good command of applying statistical methods in data analysis, handles the formulation of statistical hypotheses well, and interprets analysis results effectively.
For a grade of 5.0 (excellent): The student has mastered the application of statistical methods in data analysis very well, adeptly formulates statistical hypotheses, and interprets analysis results with great proficiency.
Competencies:
For a grade of 2.0 (unsatisfactory): The student is not able to determine priorities in task execution.
For a grade of 3.0 (satisfactory): The student is moderately capable of determining priorities in their own and others' work.
For a grade of 4.0 (good): The student handles determining priorities in their own and others' work well.
For a grade of 5.0 (excellent): The student excels at determining priorities in their own and others' work.
Practical placement
not applicable
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: