Preliminary Laboratory 1100-1AF26
The course prepares students for independent experimental work and for processing measurement data. Students perform five laboratory exercises. Topics are selected to familiarize them with basic instruments and measurement techniques, and to provide data suitable for illustrating commonly used data‑analysis methods. Classes are organized in two‑week blocks (3 hours/week). Week 1: performing measurements. Week 2: computational exercises preparing students for analysis of the collected data
Before the measurement session, the instructor checks and evaluates students’ preparation (theory and measurement procedures). Participation is mandatory. Up to two justified absences are allowed during measurement sessions.
List of laboratory exercises
- Density of solids: determining density from mass and volume measurements.
- Simple pendulum: studying oscillations and the dependence of the period on length.
- Ohm’s and Kirchhoff’s laws: measurements and verification of current–voltage relationships.
- Uniformly accelerated motion: determining the moment of inertia of a cylinder from motion on an incline.
- Thermometer: construction, calibration, and determination of instrument accuracy.
Course content
- Error analysis: types and sources of measurement errors, measurement uncertainty, propagation of small errors.
- Data statistics: mean value, standard deviation, probability distributions, dataset characteristics.
- Accuracy and calibration: metrological parameters of instruments, calibration procedures, uncertainty evaluation.
- Graphical presentation: plots, curve fitting, visual analysis of trends and residuals.
- Least‑squares method: linear and nonlinear regression, examples.
- Parameter relationships: correlations, confidence intervals, covariance matrix.
- Hypothesis testing: choice of tests, significance level, inference based on measurement data.
- Practical applications: execution of five laboratory exercises.
Forms of instruction
- Laboratory exercises: independent performance of measurements.
- Computational classes: solving tasks preparing for data analysis.
- Group discussions: discussing measurement methods and data analysis.
- Teamwork: joint execution of two out of five exercises and joint report preparation.
- Independent data analysis: preparing reports for three exercises.
- Individual consultations: short meetings with the instructor.
Teaching methods
- Experimental method
- Problem‑solving method
- Project method (preparing reports)
- Discussion method
- Practical method (working with instruments)
- Statistical methods
- Team‑based learning
Course coordinators
Mode
Prerequisites (description)
Learning outcomes
Knowledge:
- Understands basic experimental techniques necessary to plan and perform simple classical‑physics experiments.
- Understands theoretical principles of basic measurement systems and research apparatus; is aware of technological and methodological limitations.
- Knows elements of measurement‑uncertainty theory.
- Knows safety and hygiene rules required for safe participation in laboratory classes.
Skills:
- Can reproduce theorems and equations describing basic physical phenomena.
- Can plan, conduct, and interpret medium‑complexity experiments independently or in a team.
- Can prepare written reports on experimental work.
- Can collaborate in a group in various roles.
- Can set priorities for completing assigned tasks.
Social competences:
- Acts with academic integrity and promotes intellectual honesty.
- Can resolve ethical issues related to research reliability.
- Appreciates the role of experiment in verifying physical theories.
- Applies the scientific method in acquiring knowledge.
- Is ready to study scientific and popular‑science literature, recognizing risks of unverified sources.
Assessment criteria
Students complete five laboratory exercises. The first two may be performed in pairs; the remaining three must be done individually.
Reports must follow the guidelines on the course website and be submitted within five working days after the computational session.
Reports for paired exercises may be prepared jointly, with clear indication of each author’s contribution.
Use of AI tools in writing must be explicitly indicated; AI may not be used for data analysis.
Grading of reports:
- First two reports: pass/fail, with up to two revisions allowed.
- Last three reports: graded, with one revision allowed.
Point distribution per exercise
- Preparation: 10 points (test before measurements)
- Execution: 10 points (quality of experimental work)
- Report: 80 points
- Editorial quality: 20 points
- Substantive content: 60 points
- Bonus: 10 points for an exceptionally good report (≥90 points total)
To pass the course, the student must obtain a pass or positive grade for all exercises. The final grade is the average of all partial grades.
Bibliography
- J. R. Taylor, Introduction to Error Analysis, PWN, 1995.
- G. L. Squires, Practical Physics, PWN, 1992.
- H. Abramowicz, How to Analyse Measurement Results, PWN, 1992.
- A. Zięba, Data Analysis in Science and Engineering, PWN, 2013.
Supplementary:
- S. Brandt, Data Analysis, PWN, 1998.
- J. J. Jakubowski, R. Sztencel, Introduction to Probability Theory, SCRIPT, 2001.
- W. Feller, An Introduction to Probability Theory, PWN, 1977.
- R. Nowak, Statistics for Physicists, PWN, 2002.
- W. T. Eadie et al., Statistical Methods in Experimental Physics, PWN, 1989.