UCACCSTA217.5 ECTSEnglishBachelor
Biostatistics
Faculteit—
NiveauBachelor
Studiejaar2026-2027
Beschrijving
Course goals
- Understand, apply, and explain all methodological and statistical concepts used in class
- Explain the function and rationale of commonly used analysis techniques, including descriptive measures, t-tests, factorial Anova, Repeated measures Anova, Manova, measures for correlation, OLS regression, binary logistic regression, ROC curves, Chi-squared tests for goodness of fit and independence, life tables, and Cox proportional hazard regression.
- Explain, for a given research question, which choices regarding research design and analysis techniques may be made and why these are appropriate
- Conduct the procedures and analyses mentioned under 2 using JASP, interpret the output and report the findings following the APA format
- Explain selected methodological and statistical concepts by providing examples and short explanations in everyday language
- Reflect on the relevance of this biostatistics course and its components in your curriculum
Relationship between assessment and learning goals:
In this course, the final course grade is based on five elements: Three written exams, a personal electronic textbook, and a reflection document.
- Written in-class exam 1: (midterm exam) this tests your knowledge of concepts and ability to interpret JASP output as covered before midterm (course goal 1).
- Written in-class exam 2: (final exam) this tests your knowledge of concepts and ability to interpret JASP output as covered during the course, with an emphasis on the material covered after midterm before midterm (course goals 1, 2, 3, 4, and 5)
- Written in-class exam 3: (JASP skills test) this tests your ability to perform analyses in JASP and report on these (course goal 4)
- Personal Electronic Textbook (PET). In your PET, you can demonstrate your ability to explain methodological and statistical concepts using everyday language (course goal 6).
- Reflection document. After each lab session, you will write a short piece of text to reflect on the relevance of the techniques dealt with in your curriculum (course goal 7). Together, these constitute the reflection document.
Content
Please note: The content of this course overlaps with UCACCMET22/23. Do not take both.
The content of the course is partly devoted to the understanding of the fundamentals of descriptive and inferential statistics (concepts, rationale of analyses and their assumptions), and partly to the application of techniques on data sets provided by the instructor. We will start with a definition of basic concepts relevant to all statistical tests, eg chance and odds, randomness, data levels, and probability distributions. Systematic errors and random errors will be discussed in relation to their impact on the reliability and validity of data. Concepts that will be explained in relation to statistical estimation and decision theory include the sampling distribution, standard error, test statistics, chosen (alpha) and observed (p-value) significance level, type I and type II error, the power of a test, confidence intervals, and effect size measures. We will cover a few research designs that are widely used in applied science research and relate these to different types of samples.
The actual tests and analysis methods include tests for group differences (t-tests, factorial Anova, Repeated measures Anova, Manova), and tests for relations between variables, such as Chi-square tests for goodness of fit and homogeneity / independence, OLS multiple linear regression models, binary logistic regression and ROC curves, life tables and Cox proportional hazards regression.
In the lab sessions, you will be given data sets that will have to be checked and summarized using appropriate descriptive statistical techniques. Data transformations will be applied where needed. Next to these descriptive statistics, you will test specific hypotheses on the given data sets and report on their findings in lab reports.
Format
The course is based on a mixture of lectures and guided computer lab sessions.
In the lectures, you are introduced to the fundamental concepts, assumptions, and rationale of statistical analyses. Each class session, you have to complete assigned entries in a Personal Electronic Textbook (PET). Each entry contains the definition of a concept, its source, and an illustrative example provided by you. The PET serves as a guide for homework and studies, as well as a quick reference guide for future use. At the end of the course, you submit your PET for evaluation.
In six computer lab sessions, you are familiarized with statistical analysis software (JASP), and conduct analyses that were previously explained in theory. You may work individually or in pairs. Completion of all labs is mandatory. You will also write a short reflective text after each lab session.
The content of the course is partly devoted to the understanding of the fundamentals of descriptive and inferential statistics (concepts, rationale of analyses and their assumptions), and partly to the application of techniques on data sets provided by the instructor. We will start with a definition of basic concepts relevant to all statistical tests, eg chance and odds, randomness, data levels, and probability distributions. Systematic errors and random errors will be discussed in relation to their impact on the reliability and validity of data. Concepts that will be explained in relation to statistical estimation and decision theory include the sampling distribution, standard error, test statistics, chosen (alpha) and observed (p-value) significance level, type I and type II error, the power of a test, confidence intervals, and effect size measures. We will cover a few research designs that are widely used in applied science research and relate these to different types of samples.
The actual tests and analysis methods include tests for group differences (t-tests, factorial Anova, Repeated measures Anova, Manova), and tests for relations between variables, such as Chi-square tests for goodness of fit and homogeneity / independence, OLS multiple linear regression models, binary logistic regression and ROC curves, life tables and Cox proportional hazards regression.
In the lab sessions, you will be given data sets that will have to be checked and summarized using appropriate descriptive statistical techniques. Data transformations will be applied where needed. Next to these descriptive statistics, you will test specific hypotheses on the given data sets and report on their findings in lab reports.
Format
The course is based on a mixture of lectures and guided computer lab sessions.
In the lectures, you are introduced to the fundamental concepts, assumptions, and rationale of statistical analyses. Each class session, you have to complete assigned entries in a Personal Electronic Textbook (PET). Each entry contains the definition of a concept, its source, and an illustrative example provided by you. The PET serves as a guide for homework and studies, as well as a quick reference guide for future use. At the end of the course, you submit your PET for evaluation.
In six computer lab sessions, you are familiarized with statistical analysis software (JASP), and conduct analyses that were previously explained in theory. You may work individually or in pairs. Completion of all labs is mandatory. You will also write a short reflective text after each lab session.
Reviews0 reviews
Nog geen reviews voor dit vak. Wees de eerste!
Heb jij dit vak gevolgd?
Deel je ervaring met toekomstige studenten. Inloggen met je Universiteit Utrecht mailadres duurt één minuut.
Schrijf een review