Ateneo - UPO

Analysis of experimental uncertainty in single and repeated measurements. Elements of probability and statistics. Statistical tests. Evaluation of confidence intervals on results. Linear ad non-linear regresion. Use of data analysis and presentation software.

J. R. Taylor, Introduzione all'analisi degli errori - seconda edizione
R. A. Johnson, Miller and Freund's Probability and Statistics for Engineers

The purpose of the course is to learn the basic techniques of data analysis and to use softwares like excel and R at a base level. The techniques learned can be used in everyday laboratory practice and in an experimental thesis. The acquired abilities are also useful for future teachers in secondary school.

During the lessons some simple derivatives and integrals will be used.

Presentation of measurements of biological interest. How to keep a laboraory logbook. Dicussion of experimental observations and data analysis with statistical software. Communication of results as a report.

It is essential to study the theory of data analysis before attending the lab. It is necessary to attend laboratory classes.
Students with physical disabilities, Learning Disabilities or Special Education Needs can request
specific services and tools via the Staff Sviluppo e Coordinamento Carriere e Servizi alle Studentesse
e agli Studenti, consulting the University webpage: https://www.uniupo.it/en/services/services-
students-physical-or-learning-disabilities
Students with disabilities, learning disabilities or special education needs, once they have contacted
the University Staff, can refer to the tutor in charge of the course to define the examination
modalities, concerning academic aspects.

The written examination consists of short data analysis exercises completed before the start of the laboratory sessions to assess the students' ability to apply the data analysis techniques presented during the lectures. Students may bring a personally prepared formula sheet containing any formulas they consider useful.
The laboratory notebook (logbook) must provide a complete record of the experimental work, allowing the experimental procedure—including any difficulties encountered—and the acquired data to be reconstructed long after the experiment has been performed. At the end of each laboratory session, the instructor will collect a subset of the logbooks, on a rotating basis, for formative assessment.
The oral examination consists of the discussion of the written laboratory report for one of the experiments carried out during the course, together with the theoretical foundations of the data analysis techniques employed. The report should concisely state the objective of the experiment, list the materials used, describe the experimental procedure, present the collected data and the analysis performed, and discuss the results, highlighting any limitations or issues encountered and, where appropriate, proposing possible improvements.
The laboratory report must be submitted at least 15 days before the examination date to allow the instructor to provide feedback on aspects requiring revision. The final evaluation is based on the revised report discussed during the oral examination.
Examination dates are arranged directly with the instructor. The dates published in the official examination schedule are indicative only.
The written examination, laboratory notebook, laboratory report, and oral examination each contribute a maximum of 8 points to the final mark.

Measurement and experimental errors. Analysis of random errors: mean, standard deviation, and standard error of the mean. Error propagation. Probability distributions. Confidence intervals. Linear and non-linear regression.
Physical measurements of biological interest will be proposed, including, for example: calibration of a micropipette and a graduated cylinder; counting measurements; assessment of treatment effectiveness; analysis of perception thresholds; image analysis using ImageJ; and measurements on an electrical model of a neuron.

Knowledge and understanding: awareness of the existence of experimental uncertainties. Knowledge of the techniques to quantify them.
Applying knowledge and understanding: apply these techniques in a simple experimental context. Use of data analysis programs at a basic level.
Communication skills: ability to communicate experimental results in written form and in oral presentation.