
Course unit
MATHEMATICAL TOOLS FOR PHYCHOLOGISTS
PSP5070177, A.A. 2017/18
Information concerning the students who enrolled in A.Y. 2015/16
ECTS: details
Type 
ScientificDisciplinary Sector 
Credits allocated 
Educational activities in elective or integrative disciplines 
MAT/07 
Mathematical Physics 
6.0 
Mode of delivery (when and how)
Period 
First semester 
Year 
3rd Year 
Teaching method 
frontal 
Organisation of didactics
Type of hours 
Credits 
Hours of teaching 
Hours of Individual study 
Shifts 
Lecture 
6.0 
42 
108.0 
No turn 
Start of activities 
02/10/2017 
End of activities 
12/01/2018 
Examination board
Board 
From 
To 
Members of the board 
1 20171 
01/10/2017 
30/09/2018 
ZANZOTTO
GIOVANNI
(Presidente)
SPOTO
ANDREA
(Membro Effettivo)

Prerequisites:

The mathematical knowledge necessary for the admission to the undergraduate course in Psychological Science is assumed. 
Target skills and knowledge:

The probability that a woman of age 40 has breast cancer is about 1 per cent. If she has breast cancer, the probability that she tests positive on a screening mammogram is 90 percent. If she does not have breast cancer, the probability that she nevertheless tests positive is 9 percent. What are the chances that a woman who tests positive actually has breast cancer? This class presents some basic techniques for the analysis of the uncertainty inherent in statistical information, with the goal of providing a correct evaluation and communication of risk. Basic notions of elementary probability theory are introduced and discussed, and their application is illustrated in problems connected with the medical and psychological practice. 
Examination methods:

Written final exam with open questions or quizzes. Oral presentations of selected topics during class. 
Assessment criteria:

Grading is based on the results of the final written test and on the performance in the oral presentations. 
Course unit contents:

Uncertainty in statistical information. Problems related to the evaluation of risk and communication of risk. Realworld xamples. Bayesian inferences through the use of probabilities and by means of 'natural frequencies'. Suitability of the latter for a more intuitive and direct insight in both risk estimation and in a transparent representation of risk. Examples focussing on the correct judgement of the probabilistic predictive value of medical diagnostic tests, and aiming at avoiding misleading risk information. 
Planned learning activities and teaching methods:

Class lectures, with presentation of the main points mentioned above. Some theory and several examples. Recitations and exercises to complement the theoretical parts, also directly involving students in both individual and group work. The main focus is on the applications of the topics treated during the coursework. 
Additional notes about suggested reading:

Textbook and possible extra materials available through the library or online. 
Textbooks (and optional supplementary readings) 

MAIN TEXTBOOK  Gerd Gigerenzer, Calculated Risk. New York: Simon & Schuster, 2002.

Auxiliary reading material  Gerd Gigerenzer et al., Helping Doctors and Patients Make Sense of Health Statistics. : Association for Psychological Science, 2008. http://library.mpibberlin.mpg.de/ft/gg/GG_Helping_2008.pdf

Auxiliary reading material  Stephanie Kurzenhäuser, Natural frequencies in medical risk communication: improving statistical thinking in physicians and patients. Dissertation: FU Berlin, 2003. http://www.diss.fuberlin.de/diss/servlets/MCRFileNodeServlet/FUDISS_derivate_000000001633/00_kurzenhaeuser.pdf

Auxiliary reading material  M. R. Spiegel, Theory & Problems Of Probability & Statistics. New York: Schaum Mc Graw Hill, 1998.


