Introduction to Scientific Reasoning
A.Y. 2026/2027
Learning objectives
The course aims to provide the conceptual and methodological tools to understand how scientific knowledge works and to reason critically about claims that present themselves as "scientific". The purpose of the course is therefore to introduce the nature, objectives, and methods of science, the main forms of reasoning that support it (deductive, inductive, abductive, probabilistic, statistical, causal) and the philosophical models of explanation, theory, and scientific change, constantly testing them on concrete, historical, and contemporary cases. The objective is to develop an informed, aware, and non-naive attitude toward the scientific enterprise and scientific reasoning, capable of appreciating both its strengths and its limits and self-correction mechanisms, and of applying these tools to the critical reading of scientific information in daily life and public debate.
Expected learning outcomes
Knowledge and understanding (Dublin Descriptor 1)
At the end of the course, the student will be able to:
· Describe the distinctive features of scientific knowledge, the institutional norms of science, and its self-correction mechanisms;
· Illustrate the main forms of reasoning — deductive, inductive, abductive, probabilistic, statistical, and causal — and their properties;
· List the elements of experimental design (independent, dependent, and confounding variables, control, randomization, types of validity) and the characteristics of scientific models;
· Explain the main philosophical models of explanation, theory, and scientific change.
Applying knowledge and understanding (Dublin Descriptor 2)
The student will be able to:
· Apply the rules of deductive inference (modus ponens, modus tollens) and recognize related fallacies in real-world arguments;
· Analyze the design and validity of an empirical study, identifying its confounding variables and limits;
· Apply Bayes' theorem and the basic tools of descriptive and inferential statistics to concrete problems;
· Interpret and critically evaluate a scientific claim or a popular science article using a structured set of key questions.
Making judgments (Dublin Descriptor 3)
The student will be able to:
· Evaluate the soundness of an argument and discuss the distinction between science and pseudoscience;
· Formulate independent and reasoned judgments on science and society issues (scientific consensus, the role of experts, misinformation), recognizing the role of values in research.
Communication skills (Dublin Descriptor 4)
The student will be able to:
· Communicate concepts and arguments in the philosophy and methodology of science clearly and with appropriate vocabulary to both specialist and non-specialist audiences.
Learning skills (Dublin Descriptor 5)
The student will be able to:
· Independently read a philosophical text or a scientific article, applying critical reading strategies, to continue their studies in an independent manner.
At the end of the course, the student will be able to:
· Describe the distinctive features of scientific knowledge, the institutional norms of science, and its self-correction mechanisms;
· Illustrate the main forms of reasoning — deductive, inductive, abductive, probabilistic, statistical, and causal — and their properties;
· List the elements of experimental design (independent, dependent, and confounding variables, control, randomization, types of validity) and the characteristics of scientific models;
· Explain the main philosophical models of explanation, theory, and scientific change.
Applying knowledge and understanding (Dublin Descriptor 2)
The student will be able to:
· Apply the rules of deductive inference (modus ponens, modus tollens) and recognize related fallacies in real-world arguments;
· Analyze the design and validity of an empirical study, identifying its confounding variables and limits;
· Apply Bayes' theorem and the basic tools of descriptive and inferential statistics to concrete problems;
· Interpret and critically evaluate a scientific claim or a popular science article using a structured set of key questions.
Making judgments (Dublin Descriptor 3)
The student will be able to:
· Evaluate the soundness of an argument and discuss the distinction between science and pseudoscience;
· Formulate independent and reasoned judgments on science and society issues (scientific consensus, the role of experts, misinformation), recognizing the role of values in research.
Communication skills (Dublin Descriptor 4)
The student will be able to:
· Communicate concepts and arguments in the philosophy and methodology of science clearly and with appropriate vocabulary to both specialist and non-specialist audiences.
Learning skills (Dublin Descriptor 5)
The student will be able to:
· Independently read a philosophical text or a scientific article, applying critical reading strategies, to continue their studies in an independent manner.
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
blended learning
Responsible
Lesson period
First semester
Course syllabus
The programme is organised into the following thematic blocks, covered in the order indicated and in close connection with the chapters of the course textbook:
The nature and institution of science — What is science: demarcation criteria and checklists; theory and observation; fallibilism and its uses (including anti-scientific ones); science as a collective enterprise and institution; Merton's norms; scientific misconduct and conflicts of interest.
Experimental method, non-experimental inquiry, and models — Variables and experimental design; direct and indirect control, randomisation, RCTs, blinding and double-blinding; internal, external, ecological, and population validity; the WEIRD problem; observational studies and natural experiments; data models and phenomenon models, overfitting and underdetermination.
Logic and deductive reasoning — Deduction vs. induction; validity vs. soundness; conditionals, modus ponens and modus tollens and related fallacies; Hume's problem of induction; Popper's falsificationism and demarcation criterion; the confirmation/refutation asymmetry and Duhem-Quine holism; the hypothetico-deductive method.
Probabilistic and statistical reasoning — Interpretations of probability (frequentist, propensity, subjectivist); Bayes' theorem and reasoning with conditional probabilities; descriptive statistics and data visualisation; inferential statistics, the central limit theorem, confidence intervals and hypothesis testing; uses and abuses of statistics.
Explanation, theories, scientific change, and science in society — Features of scientific theories; Hempel's deductive-nomological model and its problems; causal explanation and causal reasoning; scientific revolutions and incommensurability in Kuhn; comparison of major theories; science, values, and society (gender, experts, misinformation).
The programme is the same for attending and non-attending students.
The nature and institution of science — What is science: demarcation criteria and checklists; theory and observation; fallibilism and its uses (including anti-scientific ones); science as a collective enterprise and institution; Merton's norms; scientific misconduct and conflicts of interest.
Experimental method, non-experimental inquiry, and models — Variables and experimental design; direct and indirect control, randomisation, RCTs, blinding and double-blinding; internal, external, ecological, and population validity; the WEIRD problem; observational studies and natural experiments; data models and phenomenon models, overfitting and underdetermination.
Logic and deductive reasoning — Deduction vs. induction; validity vs. soundness; conditionals, modus ponens and modus tollens and related fallacies; Hume's problem of induction; Popper's falsificationism and demarcation criterion; the confirmation/refutation asymmetry and Duhem-Quine holism; the hypothetico-deductive method.
Probabilistic and statistical reasoning — Interpretations of probability (frequentist, propensity, subjectivist); Bayes' theorem and reasoning with conditional probabilities; descriptive statistics and data visualisation; inferential statistics, the central limit theorem, confidence intervals and hypothesis testing; uses and abuses of statistics.
Explanation, theories, scientific change, and science in society — Features of scientific theories; Hempel's deductive-nomological model and its problems; causal explanation and causal reasoning; scientific revolutions and incommensurability in Kuhn; comparison of major theories; science, values, and society (gender, experts, misinformation).
The programme is the same for attending and non-attending students.
Prerequisites for admission
There are no specific prerequisites other than those required for admission to the Degree Programme
Teaching methods
09:10 Claude responded: Blended learning course:Blended learning course:
Delivered teaching activities (DE):
20 recorded lectures, supported by slides and written materials (handouts and primary sources)
3 in-person participatory lectures, supported by slides and written materials (handouts and primary sources)
7 synchronous participatory lectures, supported by slides and written materials (handouts and primary sources)
Interactive teaching (DI): multiple-choice quizzes for ongoing assessment of understanding of logical and conceptual form; guided applied exercises; structured discussions on philosophical dilemmas and case studies in science and society.
The teaching methods are consistent with the expected learning outcomes: the alternation between exposition, guided application, and critical discussion supports both the acquisition of knowledge and understanding and the development of the ability to apply it, independent judgement, and communication skills.
Delivered teaching activities (DE):
20 recorded lectures, supported by slides and written materials (handouts and primary sources)
3 in-person participatory lectures, supported by slides and written materials (handouts and primary sources)
7 synchronous participatory lectures, supported by slides and written materials (handouts and primary sources)
Interactive teaching (DI): multiple-choice quizzes for ongoing assessment of understanding of logical and conceptual form; guided applied exercises; structured discussions on philosophical dilemmas and case studies in science and society.
The teaching methods are consistent with the expected learning outcomes: the alternation between exposition, guided application, and critical discussion supports both the acquisition of knowledge and understanding and the development of the ability to apply it, independent judgement, and communication skills.
Teaching Resources
Required textbook:
A. Potochnik, M. Colombo, C. Wright, Introduzione al ragionamento scientifico. Ricette per la scienza (Italian translation by C. Sinigaglia), Raffaello Cortina Editore, Milan, 2025. ISBN 978-88-3285-826-6. (Required: Yes)
Any additional required and recommended readings, as well as further teaching materials (slides, handouts, exercises, primary sources), will be indicated and distributed by the instructor during lectures and made available on MyAriel. The reference materials are the same for attending and non-attending students.
A. Potochnik, M. Colombo, C. Wright, Introduzione al ragionamento scientifico. Ricette per la scienza (Italian translation by C. Sinigaglia), Raffaello Cortina Editore, Milan, 2025. ISBN 978-88-3285-826-6. (Required: Yes)
Any additional required and recommended readings, as well as further teaching materials (slides, handouts, exercises, primary sources), will be indicated and distributed by the instructor during lectures and made available on MyAriel. The reference materials are the same for attending and non-attending students.
Assessment methods and Criteria
The exam consists of a single written test (approximately 90 minutes), covering the entire syllabus and accounting for 100% of the grade. The test is divided into two parts:
- closed-answer section (multiple-choice and matching questions) assessing knowledge and understanding of concepts, definitions, and forms of reasoning;
- open-answer section (one or more short open questions or an applied exercise, e.g. analysing the validity of an argument, critically reading a case study) assessing skills of analysis, application, and critical evaluation.
This structure ensures constructive alignment with the expected learning outcomes: the closed section tests "knowledge/understanding" outcomes, while the open section tests "application, analysis, and evaluation" outcomes.
Optional mid-term exercises. Throughout the course, a number of optional mid-term exercises will be offered (e.g. on logic, Bayes' theorem, and critical case analysis), designed to allow students to test their understanding along the way and receive formative feedback. These exercises do not contribute in any way to the final grade and are not compulsory; participation is nonetheless strongly encouraged, as they provide a valuable opportunity for self-assessment and progressive preparation for the exam.
Grading scale and assessment type. The grade is expressed out of thirty, with possible distinction (lode), and is determined entirely by the single final written test. The test is considered passed with a minimum grade of 18/30.
Assessment criteria. The following factors contribute to the final grade: accuracy and completeness of answers; command of concepts and appropriate use of specialist terminology; ability to correctly apply logical, probabilistic, and statistical tools to new cases; quality of argumentation and independent judgement in open answers; clarity of expression. As a general guide: 18-21: essential and correct knowledge, predominantly reproductive; 22-25: good understanding, correct application to familiar cases, adequate terminology; 26-28: confident application to both familiar and new cases, articulate critical analysis; 29-30 with distinction: full command, autonomous evaluation, and ability to make original connections.
- closed-answer section (multiple-choice and matching questions) assessing knowledge and understanding of concepts, definitions, and forms of reasoning;
- open-answer section (one or more short open questions or an applied exercise, e.g. analysing the validity of an argument, critically reading a case study) assessing skills of analysis, application, and critical evaluation.
This structure ensures constructive alignment with the expected learning outcomes: the closed section tests "knowledge/understanding" outcomes, while the open section tests "application, analysis, and evaluation" outcomes.
Optional mid-term exercises. Throughout the course, a number of optional mid-term exercises will be offered (e.g. on logic, Bayes' theorem, and critical case analysis), designed to allow students to test their understanding along the way and receive formative feedback. These exercises do not contribute in any way to the final grade and are not compulsory; participation is nonetheless strongly encouraged, as they provide a valuable opportunity for self-assessment and progressive preparation for the exam.
Grading scale and assessment type. The grade is expressed out of thirty, with possible distinction (lode), and is determined entirely by the single final written test. The test is considered passed with a minimum grade of 18/30.
Assessment criteria. The following factors contribute to the final grade: accuracy and completeness of answers; command of concepts and appropriate use of specialist terminology; ability to correctly apply logical, probabilistic, and statistical tools to new cases; quality of argumentation and independent judgement in open answers; clarity of expression. As a general guide: 18-21: essential and correct knowledge, predominantly reproductive; 22-25: good understanding, correct application to familiar cases, adequate terminology; 26-28: confident application to both familiar and new cases, articulate critical analysis; 29-30 with distinction: full command, autonomous evaluation, and ability to make original connections.
Modules or teaching units
Parte A e B
PHIL-02/A - Logic and Philosophy of Science - University credits: 6
Lessons: 40 hours
Parte C
PHIL-02/A - Logic and Philosophy of Science - University credits: 3
Lessons: 20 hours
Surname A-K
Responsible
Lesson period
First semester
Modules or teaching units
Parte A e B
PHIL-02/A - Logic and Philosophy of Science - University credits: 6
Lessons: 40 hours
Professor:
Sassoli De Bianchi Bernardino Tommaso
Parte C
PHIL-02/A - Logic and Philosophy of Science - University credits: 3
Lessons: 20 hours
Professor:
Serpico Davide
Surname L-Z
Responsible
Lesson period
First semester
Course syllabus
The program is divided into the following thematic blocks, closely matching the chapters of the required textbook:
1) The Nature and Institution of Science — What science is: demarcation criteria and the "checklist"; theory and observation; fallibilism and its uses (including anti-scientific ones); science as a collective effort and institution; Merton's norms; scientific dishonesty and conflicts of interest.
2) Experimental Method, Non-Experimental Research, and Models — Variables and experimental design; direct and indirect control, randomization, RCTs, blind and double-blind studies; internal, external, ecological, and population validity; the WEIRD problem; observational studies and natural experiments; data models and models of phenomena, overfitting, and underdetermination.
3) Logic and Deductive Reasoning — Deduction vs. induction; validity vs. soundness; conditionals, modus ponens, modus tollens, and related logical mistakes; Hume's problem of induction; Popper's falsificationism and his demarcation criterion; the asymmetry of confirmation/refutation and the Duhem-Quine thesis; the hypothetical-deductive method.
4) Probabilistic and Statistical Reasoning — Interpretations of probability (frequentist, propensity, subjectivist); Bayes' theorem and reasoning with conditional probabilities; descriptive statistics and data visualization; inferential statistics, the central limit theorem, confidence intervals, and hypothesis testing; uses and abuses of statistics.
5) Explanation, Theories, Scientific Change, and Science in Society — Features of scientific theories; Hempel's deductive-nomological model and its problems; causal explanation and causal reasoning; scientific revolutions and incommensurability in Kuhn; comparing major theories; science, values, and society (gender, experts, fake news).
The program is the same for attending and non-attending students.
1) The Nature and Institution of Science — What science is: demarcation criteria and the "checklist"; theory and observation; fallibilism and its uses (including anti-scientific ones); science as a collective effort and institution; Merton's norms; scientific dishonesty and conflicts of interest.
2) Experimental Method, Non-Experimental Research, and Models — Variables and experimental design; direct and indirect control, randomization, RCTs, blind and double-blind studies; internal, external, ecological, and population validity; the WEIRD problem; observational studies and natural experiments; data models and models of phenomena, overfitting, and underdetermination.
3) Logic and Deductive Reasoning — Deduction vs. induction; validity vs. soundness; conditionals, modus ponens, modus tollens, and related logical mistakes; Hume's problem of induction; Popper's falsificationism and his demarcation criterion; the asymmetry of confirmation/refutation and the Duhem-Quine thesis; the hypothetical-deductive method.
4) Probabilistic and Statistical Reasoning — Interpretations of probability (frequentist, propensity, subjectivist); Bayes' theorem and reasoning with conditional probabilities; descriptive statistics and data visualization; inferential statistics, the central limit theorem, confidence intervals, and hypothesis testing; uses and abuses of statistics.
5) Explanation, Theories, Scientific Change, and Science in Society — Features of scientific theories; Hempel's deductive-nomological model and its problems; causal explanation and causal reasoning; scientific revolutions and incommensurability in Kuhn; comparing major theories; science, values, and society (gender, experts, fake news).
The program is the same for attending and non-attending students.
Prerequisites for admission
There are no specific prerequisites other than those required for admission to the Degree Programme.
Teaching methods
In-person teaching (Type Alpha).
- Direct Teaching (DE): Interactive lectures supported by slides and text materials (handouts and primary sources). Abstract concepts are always linked to historical and modern case studies.
- Interactive Teaching (DI): Multiple-choice quizzes in class to check understanding of logic and concepts. Guided practical exercises and structured discussions on philosophical dilemmas and science-and-society topics.
The teaching methods match the expected learning outcomes. Switching between lectures, guided practice, and critical discussions helps students gain knowledge and understanding. It also helps develop the ability to apply skills, form independent judgments, and improve communication skills.
Recap activities of the program to be held online could be implemented for a maximum of 4 hours.
- Direct Teaching (DE): Interactive lectures supported by slides and text materials (handouts and primary sources). Abstract concepts are always linked to historical and modern case studies.
- Interactive Teaching (DI): Multiple-choice quizzes in class to check understanding of logic and concepts. Guided practical exercises and structured discussions on philosophical dilemmas and science-and-society topics.
The teaching methods match the expected learning outcomes. Switching between lectures, guided practice, and critical discussions helps students gain knowledge and understanding. It also helps develop the ability to apply skills, form independent judgments, and improve communication skills.
Recap activities of the program to be held online could be implemented for a maximum of 4 hours.
Teaching Resources
Mandatory Textbook:
A. Potochnik, M. Colombo, C. Wright, Introduzione al ragionamento scientifico. Ricette per la scienza (Italian translation by C. Sinigaglia), Raffaello Cortina Editore, Milan, 2025. ISBN 978-88-3285-826-6. (Required: Yes)
Any additional required or recommended readings, as well as further teaching materials (slides, handouts, exercises, primary sources), will be announced and shared by the instructor during classes and made available on MyAriel. The reference material is exactly the same for attending and non-attending students.
A. Potochnik, M. Colombo, C. Wright, Introduzione al ragionamento scientifico. Ricette per la scienza (Italian translation by C. Sinigaglia), Raffaello Cortina Editore, Milan, 2025. ISBN 978-88-3285-826-6. (Required: Yes)
Any additional required or recommended readings, as well as further teaching materials (slides, handouts, exercises, primary sources), will be announced and shared by the instructor during classes and made available on MyAriel. The reference material is exactly the same for attending and non-attending students.
Assessment methods and Criteria
The exam is a single final written test (about 90 minutes long). It covers the whole program and counts for 100% of the final grade. The test has two parts:
1) A closed-answer part (multiple-choice and matching questions). This tests your knowledge and understanding of concepts, definitions, and types of reasoning.
2) An open-answer part (short open questions or a practical exercise, such as checking if an argument is valid or reading a case study). This tests your skills in analysis, application, and critical evaluation.
This setup matches the expected learning outcomes (constructive alignment). The closed part tests "knowledge and understanding." The open part tests "application, analysis, and evaluation."
Optional Mid-term Exercises
During the course, there will be some optional mid-term exercises (for example, on logic, Bayes' theorem, and critical case analysis). These help students test their understanding and get feedback as they go. These exercises do not count toward the final grade and are not mandatory. However, we strongly recommend taking part. They are a great way to check your progress and prepare for the final exam.
Grading and Evaluation Type
The grade uses a 30-point scale (from 18 to 30), with the possibility of honors (lode). The grade depends entirely on the single final written test. You pass the exam with a minimum score of 18/30.
Evaluation Criteria
Your grade will depend on:
- Accurate and complete answers.
- Good use of concepts and correct technical words.
- The ability to apply tools of logic, probability, and statistics to new cases.
- Clear arguments and independent thinking in the open answers.
- Clear writing.
As a general guide:
- 18-21: Basic and correct knowledge, mostly repeating facts.
- 22-25: Good understanding, correct application to known cases, and appropriate vocabulary.
- 26-28: Confident application to both known and new cases, with detailed critical analysis.
- 29-30 and lode (honors): Full mastery, independent judgment, and original connections between ideas.
Allowed Tools
You can use a non-programmable calculator. You cannot use notes, books, or any devices connected to the internet.
The exam rules are exactly the same for students who attend classes and those who do not.
1) A closed-answer part (multiple-choice and matching questions). This tests your knowledge and understanding of concepts, definitions, and types of reasoning.
2) An open-answer part (short open questions or a practical exercise, such as checking if an argument is valid or reading a case study). This tests your skills in analysis, application, and critical evaluation.
This setup matches the expected learning outcomes (constructive alignment). The closed part tests "knowledge and understanding." The open part tests "application, analysis, and evaluation."
Optional Mid-term Exercises
During the course, there will be some optional mid-term exercises (for example, on logic, Bayes' theorem, and critical case analysis). These help students test their understanding and get feedback as they go. These exercises do not count toward the final grade and are not mandatory. However, we strongly recommend taking part. They are a great way to check your progress and prepare for the final exam.
Grading and Evaluation Type
The grade uses a 30-point scale (from 18 to 30), with the possibility of honors (lode). The grade depends entirely on the single final written test. You pass the exam with a minimum score of 18/30.
Evaluation Criteria
Your grade will depend on:
- Accurate and complete answers.
- Good use of concepts and correct technical words.
- The ability to apply tools of logic, probability, and statistics to new cases.
- Clear arguments and independent thinking in the open answers.
- Clear writing.
As a general guide:
- 18-21: Basic and correct knowledge, mostly repeating facts.
- 22-25: Good understanding, correct application to known cases, and appropriate vocabulary.
- 26-28: Confident application to both known and new cases, with detailed critical analysis.
- 29-30 and lode (honors): Full mastery, independent judgment, and original connections between ideas.
Allowed Tools
You can use a non-programmable calculator. You cannot use notes, books, or any devices connected to the internet.
The exam rules are exactly the same for students who attend classes and those who do not.
Modules or teaching units
Parte A e B
PHIL-02/A - Logic and Philosophy of Science - University credits: 6
Lessons: 40 hours
Parte C
PHIL-02/A - Logic and Philosophy of Science - University credits: 3
Lessons: 20 hours
Professor(s)
Reception:
During the second semester, please email me to arrange a meeting.
Department of Philosophy, via Festa del Perdono 7, Cortile Ghiacciaia, 3Floor and/or Teams Platform
Reception:
Mondays 14.30-15.30 or by appointment
Office (last floor, Via Festa del Perdono 7), or online