Course manual 2019/2020
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
- Describe properties of individual variables and relations among variables accurately in text, tables and graphs.
- Make tables that describe properties of individual variables and/or relations among variables accurately.
- Make graphs that describe properties of individual variables and/or relations among variables accurately.
- Apply the null-hypothesis significance testing (NHST) method to test the difference between two or more means or proportions for a given data set.
- Apply the NHST method to test for independence among two categorical variables for a given data set.
- Apply a linar model (multiple linear regression model) to infer relations among a single response and multiple predictor variables and make predictions.
- Interpret the results of NHST and linear models to make data-based decisions in relation to a research question.
- Report the results of NHST and linear models correctly, effectively, and in context without relying on statistical jargon.
- Report the results of NHST and 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 main weaknesses of NHST in scientific research.
- Recognize and explain the main weaknesses of NHST in scientific research.
Teaching methods
- Computer lab session/practical training
- Self-study
All the material is set up for self-study with theory and corresponding exercises (in SOWISO). After going over the self-study material individually, there is a contact moment where you can ask questions and practice with more in-depth exercises. There are two contact moments every week (Monday and Thursday). Attendance and active participation in these meetings is not mandatory but strongly recommended.
Learning activities
|
Activity
|
Hours
|
|
Digital Test
|
2
|
|
Laptop lecture
|
16
|
|
Question hour
|
2
|
|
Self study
|
64
|
|
Total
|
84
|
(3 EC x 28 hr)
|
Attendance
Programme's requirements concerning attendance (OER-B):
-
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 90% of the tutorials/seminars and to prepare himself adequately, unless indicated otherwise in the course manual. In case the student attends less than 90%, the course cannot be finished
Additional requirements for this course:
Attendance at the lectures is not mandatory. However, we strongly advise everyone to actively participate and be present at the lectures.
Assessment
To pass this course, you have to correctly answer 55% or more of the questions.
The exam will contain both theoretical questions and questions that require (computer) calculations. An example exam is part of the course material at https://uva.sowiso.nl
The exam will take 2 hours in a digital test room where you will use R with RStudio and can use the formula + R-help booklet, but not any other resources.
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'.
Inspection of assessed work
The exam results can be inspected on 27 March 10:00-11:00 (the location will be communicated by email).
Students can signup for the inspection session until 26 March 9:00 on this form
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
- Before the course starts - catch-up on descriptive stats & hypothesis testing (VVA) 4 hr self study
- Monday - study ‘Comparing two groups’, ask questions in class 2 hr self study, 2 hr class
- Tuesday & Wednesday - study & make exercises ‘Comparing two Groups’ 6 hr self study
- Thursday - revise ‘Comparing two Groups’, ask questions in class 2 hr self study, 2hr class
- Friday - study & make exercises ‘Categorical Association’ 6 hr self study
Week 2
- Monday - make exercises and revise ‘Categorical Association’, ask questions 2 hr self study, 2hr class
- Tuesday & Wednesday - study & make exercises ‘Univariate Regression’ 6 hr self study
- Thursday - revise ‘Univariate Regression’, ask questions 2 hr self study, 2hr class
- Friday - study & make exercises ‘Multiple Regression’, make exercises 6 hr self study
Week 3
- Monday - make exercises and revise ‘Multiple regression’, ask questions 2 hr self study, 2hr class
- Tuesday & Wednesday - study & make exercises ‘Analysis of Variance’ 6 hr self study
- Thursday - revise ‘Analysis of Variance, ask questions 2 hr self study, 2hr class
- Friday - study & make exercises ‘Non-parametric tests’ 6 hr self study
Week 4
- Monday - make exercises and revise ‘Non-parametric Tests’, ask questions 2 hr self study, 2hr class
- Tuesday & Wednesday - study & make exercises ‘Finding the Right Test’ 6 hr self study
- Thursday - make exercises and revise ‘Finding the Right Test’, ask questions 2 hr self study, 2hr class
Week 7
- Monday - question-hour (questions should be sent before the meeting and are posted here) 1-2 hr
Week 8
The schedule for this course is published on DataNose.
Last year's course evaluation
This is a new course so we don't have any course evaluation info from previous years.
Coordinator
The main contact for this course is through Mike Martinius - if you have any questions, send an email to m.l.martinius@uva.nl