Course manual 2020/2021
Course content
The statistical knowledge from the first year is refreshed, extended and applied to realistic problems in the domains of earth science, ecology and environmental science.
The methods of null hypothesis significance testing and linear models are covered, with an emphasis on correct choice of method for a given problem description and data set and an appropriate interpretation of the results. Also, the weaknesses of these classical statistical approaches are discussed.
In addition to inference, the accurate reporting of data (in text, tables and graphs) as well as results from statistical analyses gets attention.
Study materials
Literature
Syllabus
Practical training material
Software
Objectives
- Understand the role of statistical questions, analysis techniques and models in the empirical research cycle.
- Apply NHST and generalized linear models to test statistical hypotheses and make predictions.
- Interpret the results of NHST and gereralized linear models to make data-based decisions in relation to a research question.
- Report the results of NHST and generalized linear models correctly, effectively, and in context without relying on statistical jargon.
- Choose an appropriate statistical analysis technique based on a problem description and properies of the available data.
- Recognize and explain the strengths and weaknesses of NHST in scientific research.
- Recognize and explain the strengths and weaknesses of information-criteria based model selection in scientific research.
Teaching methods
- Computer lab session/practical training
- Self-study
Each week we will cover a new topic. Each Monday there is an online computer practical (via zoom) where students work through the study material and questions and instructors are available to explain concepts and assist with practice problems. Tuesday and Wednesday students continue to independently work through the course material, and take a practice quiz each Thursday. Throughout the week students can post questions in the Canvas "Discussions" board, and the coordinator will answer these questions via Zoom on Thursdays (sessions will be recorded and posted on Canvas for those who cannot attend). On Fridays students will begin to read and watch the course materials for the next week's topic.
Learning activities
|
Activity
|
Hours
|
|
Digital Test
|
2
|
|
Laptop lecture
|
16
|
|
Question hour
|
6
|
|
Self study
|
60
|
|
Total
|
84
|
(3 EC x 28 hr)
|
Attendance
Programme's requirements concerning attendance (OER-B):
-
Participation in fieldwork is compulsory and cannot be replaced by assignments or other courses.
-
In case of practical sessions, the student is obliged to attend at least of 90% of the sessions and to prepare himself adequately, unless indicated otherwise in the course manual. In case the student attends less than 90%, the practical sessions should be redone entirely.
-
In case of tutorials/seminars with assignments, the student is obliged to attend at least 7 out of 8 seminars and to prepare thoroughly for these meetings, unless indicated otherwise in the course manual. If the course has more than 8 seminars, the student can miss up to 1 extra meeting for every (part of) 8 tutorials/seminars. If the students attends less than the mandatory tutorials/seminars, the course cannot be completed.
Assessment
Assessment diagram
Every course goal will be assessed with an equal number of questions in the digital exam.
Students that were enrolled in the course in previous years
There are no special rules for students who have taken the previous course 'From Analyisis to Evidence'.
Assignments
There are no assignments in the course.
Fraud and plagiarism
The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl
Course structure
The topics in the course are covered in the following order:
Week 1 (Comparing two Groups)
- Before the course starts - catch-up on descriptive stats & hypothesis testing (VVA) 2 hr self study
- Monday - study ‘Comparing two groups’, ask questions in class 1 hr self study, 2 hr class
- Tuesday & Wednesday - study, make exercises post questions for ‘Comparing two Groups’ 4 hr self study
- Thursday - take ‘Comparing two Groups’, quiz ask questions in QA session hr self study, 1hr class
- Friday - study & make exercises ‘Univariate Regression’ 3 hr self study
Week 2 (Univariate Regression)
- Monday - make exercises and revise ‘Univariate Regression', ask questions 1 hr self study, 2hr class
- Tuesday & Wednesday - study, make exercises, post questions for ‘Univariate Regression’ 4 hr self study
- Thursday - take ‘Univariate Regression’, quiz ask questions in QA session 1 hr self study, 1hr class
- Friday - study & make exercises ‘Multiple Regression’, make exercises 3 hr self study
Week 3 (Multiple regression)
- Monday - make exercises and revise ‘Multiple regression’, ask questions 1 hr self study, 2hr class
- Tuesday & Wednesday -tudy, make exercises, post questions for ‘Multiple regression’ 4 hr self study
- Thursday - take ‘Multiple regression' quiz, ask questions 1 hr self study, 1hr class
- Friday - study & make exercises ‘ Analysis of Variance ’ 3 hr self study
Week 4 (Analysis of Variance)
- Monday - make exercises and revise ‘Analysis of Variance', ask questions 1 hr self study, 2hr class
- Tuesday & Wednesday - tudy, make exercises, post questions for ‘Analysis of Variance’ 4 hr self study
- Thursday - take ‘Analysis of Variance’, ask questions 1 hr self study, 1hr class
- Friday - study & make exercises ‘Categorical Association’ 3 hr self study
Week 5 (Categorical Association)
- Monday - make exercises and revise ‘Categorical Association', ask questions 1 hr self study, 2hr class
- Tuesday & Wednesday - tudy, make exercises, post questions for ‘Categorical Association’ 4 hr self study
- Thursday - take ‘Categorical Association’ quiz, ask questions 1 hr self study, 1hr class
- Friday - study & make exercises ‘Non-parametric tests’ 3 hr self study
Week 6 (Non-parametric tests)
- Monday - make exercises and revise ‘Non-parametric tests', ask questions 1 hr self study, 2hr class
- Tuesday & Wednesday - tudy, make exercises, post questions for ‘Non-parametric tests 4 hr self study
- Thursday - take ‘Non-parametric tests' quiz, ask questions 1 hr self study, 1hr class
- Friday - study & make exercises ‘Finding the Right Test’ 3 hr self study
Week 7 (Finding the Right Test)
- Monday - make exercises and revise ‘Non-parametric tests', ask questions 1 hr self study, 2hr class
- Tuesday & Wednesday - review all course content and quizzes 4 hr self study
- Thursday - review session 2 hr
Week 8
The schedule for this course is published on DataNose.
Coordinator
Staff
- Renske Hoondert
- Simon Stuij