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Course module: UCACCSTA21
UCACCSTA21
Biostatistics
Course info
Course codeUCACCSTA21
EC7.5
Course goals
The UCSCIMET21 course is intended for science students (eg life and medical sciences, earth science, sustainability, cognitive sciences) who need knowledge of methods and statistics. The aims are:
  • to help students understand and critically evaluate primary research literature with respect to the statistical methods and resulting outcomes, and
  • to prepare students to conduct commonly used analysis techniques and report on these using realistic data.
 
After the course, students will be familiar with the basic concepts and rationale of descriptive and inferential statistics and will have had hands-on experience with a variety of statistical analysis techniques commonly applied sciences in research.
Content
Depending on the specific topic, this course is strongly recommended for students writing a thesis in COG, BIO, EAR or MED.
The content of this course overlaps with UCACCMET22/23. Do not take both.

Content:

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 briefly discuss the difference between probability and non-probability samples, and cover a few designs that are widely used in applied science research.  
The actual tests and analysis methods that we will deal with include tests for group differences (factorial Anova, Analysis of covariance, repeated measures analysis, mixed designs), and tests for relations between variables, such as OLS multiple linear regression models, binary logistic regression, and Cox proportional hazards regression. Chi-square tests for tabulated data, life tables and ROC curves are also dealt with.  

In the lab sessions, students 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, students will test specific hypotheses on the given data sets and report on their findings in a lab report. 
 

Format

The course is based on lectures and guided computer lab sessions. Knowledge of relevant concepts and theories is tested with three in-class written exams.

Lectures: In the lectures, students are introduced to the fundamental concepts, assumptions, and rationale of statistical analyses. Active students participation is encouraged by following a problem-based approach where possible. Each class session, students have to complete assigned entries in a Personal Electronic Textbook (PET). Each entry contains the definition of a concept, its source, and an explanation or illustration by the student. 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, students submit their PET for evaluation (15% of final grade).

Computer lab sessions: In the computer lab sessions, students are familiarized with statistical analysis software (SPSS), and conduct analyses that were previously explained in theory. Students work in pairs, assigned by the instructor. Each lab session results in a lab report, which includes a description of the data, relevant analysis output (graphs and tables, assumptions checks), plus a description of results in a format suitable for publication. Each of these six reports contributes 5% to the final course grade.

Exams: in the exams, students will have to demonstrate their understanding of the rationale of the analysis techniques and their ability to interpret and report on analysis output.
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Kies de Nederlandse taal