The P1 takes place in the winter semester. We offer 15 experiments in 5 subject areas of classical physics, from which we make a selection before the semester starts. You will be assigned to the experiments in a rotating procedure and will carry out each selected experiment. A preliminary experiment on data evaluation precedes the actual experiments. All experiment teams conduct the preliminary data analysis experiment together with their tutors. After completion, we publish a sample report that can serve as a template for the reports of the subsequent experiments.
There are one or two dates (after the regular course has ended) available for you to make up for missed experiments.

Below you will find the most important information about the P1 experiments and their execution compiled:

• Introduction slides (German), slides of the lecture Einführung in die Datenanalyse im Praktikum (German)
• Overview of dates
• List of supervisors
• Group assignments
• General schedule (split by Mon, Thu)
• Link on ILIAS (Mon, Thu). The registration as a member is done by the internship supervisor after the preliminary meeting for the internship.

The P2 takes place in the summer semester. We offer 11 experiments in 7 subject areas of classical physics, to which you will be automatically assigned in a rotating procedure. There are one or two dates (after the regular course has ended) available for you to make up for missed experiments.

Below you will find the most important information about the P2 experiments and their execution compiled:

• Introductory slides
• Overview of the dates
• Supervisor list
• Group assignments
• General schedule (split by Mon, Thu)

The experiments of the lab course are located in the single-story building of the Physics department:

Bldg. 30.22
Engesserstrasse 7
D-76131 Karlsruhe

The lab course experiments are situated on the first and second floors of the building, as shown in the following floor plans. The capacity of the lab course is 168 spots.

In the P1 and P2 internship, in addition to specifying a measured value, we generally require the specification of a measurement uncertainty for each value you determine, which can be interpreted as a statistical confidence interval. You may have arrived at the indication of such uncertainty in various ways:

• The best way to arrive at measurement uncertainty is to determine it from the variance of a statistical ensemble test.
• If this is not possible for you, it is up to you to assess the uncertainty to the best of your knowledge and belief. You should not estimate too aggressively but also not too conservatively.
• As far as the return values of a measuring device are concerned, you may be able to rely on the manufacturer's information as an authority. To do this, check the data sheets of the relevant devices. Whenever you seek the authority of third parties, you must properly cite and reference them in your evaluation.
• An uncertainty can be derived from a function of random variables subject to uncertainty by error propagation.
• An uncertainty can be the output of a statistical parameter estimate.

Below we provide some useful links to documents and educational materials that will help you familiarize yourself with the methods of statistical parameter estimation:

• You can find the lecture Einführung in die Datenanalyse im Praktikum (German) here.
• You can find a collection of documented Jupyter-notebook instructions for learning while doing here.
• This collection also refers to the document Data evaluation in basic physics lab courses, as well as the script function fitting with the chi-square method, from which you can find out the basics of statistical data processing.
• Information on the measurement process and parameter estimation of statistical models can be found in the documentation for the preliminary data processing test using the example of the P1 pendulum.
• You can find a link to the lecture material of the CgDA course of the last semester here (account: student, password: student).

During your studies with a physics major, we offer you the following lectures in which you can learn everything important about this topic:

• Computer-aided data analysis;
• Computer use;
• Modern methods of data analysis.