LEARNING OUTCOMES
The objectives are to learn to evaluate statistical analyses critically, to learn to avoid typical pitfalls in simple statistical analyses and to learn to improve presentation of the results obtained in statistical analyses. The objective is not to learn to lie with statistics, but to learn to spot if there is something fishy in a statistical analysis. The ultimate goal is to learn to tell the truth with statistics.
Credits: 5
Schedule: 23.10.2023 - 29.11.2023
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Pauliina Ilmonen
Contact information for the course (applies in this implementation):
Pauliina Ilmonen, pauliina.ilmonen@aalto.fi
CEFR level (valid for whole curriculum period):
Language of instruction and studies (applies in this implementation):
Teaching language: English. Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
valid for whole curriculum period:
During this course, students talk about typical problems and faults in sample selection, choices of location measure, graphical presentation of data, forming questionnaires, statistical testing, and regression analysis. Students are assumed to be familiar with these methods before attending the course. The focus will be on examples about using these methods wrongly --- either accidentally or on purpose --- and on improving statistical analyses.
applies in this implementation
Lecture assignments
There is an assignment related to almost every lecture. Submit your assignments on time! Late submission is not possible! For lecture 2, every student has to come up with at least two possible project work topics. On lecture 2, we discuss about the topics and every student selects his/her topic or project group. Project work presentations take place on Lecture 10 and 11 so there is plenty of time to prepare for that. For Lecture 3, every student has to find one real data example or invent two examples that illustrate the problems related to biased samples. For Lecture 4, every student has to find one real data example or simulate two examples, where different location and/or scatter measures tell completely different stories. For Lecture 5, every student has to find one real data example or simulate two examples about misleading graphical presentation. For Lecture 6, every student has to find one real data example or simulate two examples, where results of statistical testing are false or misleading. For Lecture 7, every student has to find one real data example or simulate two examples, where regression analysis gives misleading results. For Lecture 8, every student has to find one real data example or write two made up examples of badly worded questionnaire questions or answer choices. For Lecture 9, every student has to give one example or simulate two examples related to misleading interpretation, analysis or comparison of data that is related to some timely topic.
Examples and ways to improve statistical analyses are discussed during the lectures.
Study journal
In order to complete the course, students have to keep a study journal (approximately 1/2 pages per lecture). Study journal must be submitted on time! Writing and submitting the study journal on time is compulsory for completing the course!
Assessment Methods and Criteria
valid for whole curriculum period:
The evaluation is based on attendance, lecture assignments, compulsory study journal and project work presentations.
applies in this implementation
The assessment is based on attendance, the lecture assignments, compulsory study journal and the project work. Writing and submitting the study journal on time is compulsory for completing the course! Final grade of the course is given by
grade = 5 - 0.5ms - 0.5ma - 1md - 1ij,
where ms is the number of the student's missed lectures, ma is the number of the student's missed lecture assignments, md is 1 if the student does not present his/her project work (and 0 if the student does present his/her project work), and ij is 1 if the student's study journal is incomplete (and 0 if the study journal is complete). The grades are rounded up to the closest integer. For example, grade 5 may be obtained by full attendance, completing all but one lecture assignments, submitting a complete study journal on time and presenting the project work. Grade 3 may be obtained by full attendance, completed lecture assignments, and submitting an incomplete study journal on time. Grade 1 may be obtained by attending all but 2 lectures, completing all but 2 lecture assignments, and submitting an incomplete study journal on time.
Workload
valid for whole curriculum period:
The course consists of 12 lectures, lecture assignments, project work and study journal. Majority of the lectures, instead of traditional lecturing, consists of discussions. Students find problematic data examples themselves and their findings and ideas for improving data analyses are discussed during the lectures. Students also learn to defend their ideas and discoveries by conducting their project works, where statistical analyses are used in justifying opinions and claims. In addition, students write a study journal. In the study journals students may write down notes about their thoughts and reactions to what has been discussed. Writing and submitting a study journal on time is compulsory for completing the course.
Majority of students' workload will come from independent assignments. Lecture assignments will take on average 7*10 = 70 hours to complete. That includes finding representative data examples and observing problems in them. Writing the study journal takes on average 20-25 h as total. Project work will take on average about 15-25 h. Attending the lectures takes as total 24 h.
applies in this implementation
Majority of students' workload comes from independent assignments. Lecture assignments take on average 7*8 = 56 hours to complete. That includes finding representative data examples and observing problems in them. Writing the study journal takes on average 20-25 h as total. Working on the project work takes on average about 15-20 h. Attending the lectures takes as total 24 h.
DETAILS
Study Material
applies in this implementation
Main materials for this course are the examples given by the students.
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language : English
Teaching Period : 2022-2023 Autumn II
2023-2024 Autumn IIEnrollment :
Registration for Courses: In Sisu (sisu.aalto.fi).
applies in this implementation
Note that all the lectures are given on campus. Remote attendance is not possible.
Details on the schedule
applies in this implementation
Lecture topics
Lecture 1: Introduction --- We talk about the project works and about all the lecture assignment and about common errors and problems that are related to the lecture assignment topics.
Lecture 2: Getting ready for the project works
Lecture 3: Sampling
Lecture 4: Measures of location and scatter
Lecture 5: Graphics
Lecture 6: Testing
Lecture 7: Regression analysis
Lecture 8: Questionnaires
Lecture 9: Statistics related to timely topics (climate change, current conflicts,...)
Lecture 10: Project work presentations
Lecture 11: Project work presentations
Lecture 12: Summary