11406 - Statistics for Experimental Research

Learning outcomes

At the end of the course the student will be able to understandthe usefulness of the application of correct statistical tools fora research plan, to recognize the correct statistical tests to be used in the main research contexts and to correctly interpret statistical data reported in the scientific literature related to physiotherapy.

Course contents

The course provides the fundamental statistical knowledge required to understand, analyse and interpret data generated in biomedical and clinical research, promoting the development of competencies necessary for the critical appraisal of scientific literature and the design of experimental studies.

The course covers the following topics:

• principles of biostatistics and the role of statistics in experimental research;
• classification of variables, measurement scales and statistical distributions;
• graphical presentation of data and interpretation of tables and graphs;
• descriptive statistics: measures of central tendency, variability and data distribution;
• inferential statistics: populations, samples, confidence intervals and hypothesis testing;
• the main statistical methods for group comparisons (t-test, ANOVA, ANCOVA and non-parametric tests);
• correlation analysis and linear regression;
• logistic regression and the main models for analysing associations;
• diagnostic accuracy: sensitivity, specificity, predictive values and ROC curve analysis;
• methodological principles of systematic reviews and meta-analyses, with particular emphasis on the interpretation of statistical findings;
• introduction to the use of STATA SE for data analysis, generation of tables and graphs, and interpretation of statistical outputs.

The course also promotes the development of transferable skills related to the critical interpretation of statistical results reported in scientific literature and to the selection of the most appropriate analytical methods according to the research question and study design.

Readings/Bibliography

Main textbooks

•  Norman G.R., Streiner D.L. Biostatistics: The Bare Essentials. 

For each topic, additional scientific papers, methodological guidelines and learning materials will be provided through the University's Virtual Learning Environment (Virtuale) and will be accessible via the University Library System.

 

Further readings

Additional references will be recommended throughout the course according to the topics covered and the statistical applications presented.

Teaching methods

The course is delivered by in-presence interactive teaching.

Teaching activities include:

• interactive lectures supported by multimedia presentations;
• guided demonstrations of the main statistical procedures using STATA SE;
• critical interpretation of statistical outputs generated by the software;
• practical exercises focused on selecting the most appropriate statistical test according to the research question and the characteristics of the data;
• discussion of examples drawn from the biomedical scientific literature.

Teaching materials, worked examples and scientific articles will be made available through the University's Virtual Learning Environment (Virtuale).

Assessment methods

Learning assessment consists of a written examination including multiple-choice questions covering all topics addressed during the course.

The examination is designed to assess:

• knowledge of the main concepts of descriptive and inferential statistics;
• the ability to identify the most appropriate statistical test according to the study design and data characteristics;
• the ability to correctly interpret statistical results, tables and graphical outputs;
• understanding of the principal measures of diagnostic accuracy;
• the ability to critically interpret statistical findings reported in scientific literature.

The examination lasts about 30 minutes.

During the examination, the use of textbooks, lecture notes, electronic devices or any other supporting material is not permitted.

The final mark is awarded on a 30-point scale, and the examination is passed with a minimum score of 18/30.

Assessment criteria are as follows:

• 18–21: basic knowledge of the principal statistical concepts with limited interpretative ability;
• 22–24: good knowledge of the course contents and correct interpretation of the main statistical analyses;
• 25–27: good ability to select appropriate statistical methods and critically interpret statistical findings;
• 28–30: excellent understanding of statistical methods and appropriate application to the interpretation of biomedical research;
• 30 with honours: comprehensive knowledge, outstanding critical interpretation skills, excellent integration of methodological concepts, and accurate use of statistical terminology.

No mid-term assessments or pre-examination assignments are required.

 

Use of Artificial Intelligence

With regard to assessment, the use of Artificial Intelligence (AI) tools is prohibited. Any use of AI during the examination constitutes a breach of academic integrity.

 

Students with temporary or permanent disabilities or specific learning disorders (SLD) are encouraged to contact the University's support services well in advance so that appropriate accommodations can be arranged, consistently with the learning outcomes of the course.

Teaching tools

The following learning resources will be made available through the University's Virtual Learning Environment (Virtuale):

• lecture slides;
• STATA SE download
• sample datasets;
• guided exercises;
• STATA SE scripts and statistical outputs;
• scientific articles;
• methodological guidelines;
• additional learning resources.

The use of STATA SE constitutes an integral component of the course and will be employed to demonstrate the main statistical procedures and to facilitate the interpretation of statistical outputs.

Generative Artificial Intelligence may be used as a study support tool, for example to clarify statistical concepts, summarise learning materials or facilitate self-assessment. Students remain responsible for critically evaluating the methodological and statistical accuracy of AI-generated outputs before incorporating them into their learning process.

 

Students with learning disorders and\or temporary or permanent disabilities: please, contact the office responsible (https://site.unibo.it/studenti-con-disabilita-e-dsa/en/for-students) as soon as possible so that they can propose acceptable adjustments. The request for adaptation must be submitted in advance (15 days before the exam date) to the lecturer, who will assess the appropriateness of the adjustments, taking into account the teaching objectives.

Office hours

See the website of Andrea Turolla