Statistical Methods
Michelle R. Greene
Fall 2019
Instructor: Michelle R. Greene, Ph.D
Email: mgreene2@bates.edu
Office hours: Option 1: Book me via calendar
Option 2: Stop by Hathorn 106 - I’m happy to meet when my door is open!
Logistics: M/W 9:30pm - 10:50 (Carnegie 113)
Prerequisites: Any 100-level course in Neuroscience, Biology, or Psychology
Image by artist Laurie Frick who visualized statistics of her EEG.
Course Description
This course provides a hands-on introduction to modern statistical methods for brain and behavioral data. Topics include descriptive statistics, introductory probability theory, and statistical inference using both frequentist (hypothesis tests and confidence intervals) and Bayesian approaches, regression, prediction, analyses of variance, and resampling techniques including bootstraping. Particular emphasis is placed on design choices for reproducible research. Lectures are interactive, using the R programming language. No prior programming experience is required.
Introduction
“Statistics is the grammar of science.” - Karl Pearson
We live in an uncertain world. In every facet of our lives, we make predictions about the future based on our past experiences. However, our minds are prone to a number of well-established biases that keep us from making optimal predictions. For example, we fear plane crashes and terrorist attacks over car crashes and heart disease, despite the latter being far more common. Despite its mixed reputation, statistics is the art of quantitatively learning from data. Wait, art? Really?? Yes, really. When done right, statistics can both support your claims and illuminate the subject matter.
Learning statistics will allow you to be a better decision maker in all aspects of your life. Not only will you use this content in the rest of your Bates career as you read more sophisticated academic papers and when you write your thesis, but statistical reasoning is at the heart of many careers from medicine to public policy and beyond.
Learning Objectives
After this course, you will be able to:
Given a set of data, produce appropriate tabular and graphical summaries of the variables. and give an accurate verbal interpretation of the results.
Have a working knowledge of probability theory: compute probabilities for disjoint and independent events, compute and interpret conditional probabilities.
For a given research question, formulate null and alternative hypotheses. Describe the logic behind null-hypothesis significance testing, and be able to choose the appropriate statistical test for a given question.
Define a statistical model, be able to provide concrete examples of statistical models, and apply the appropriate model type to a variety of datasets. Be able to compute and discuss the degree of model fit, including recognizing the possibility of overfitting.
Classroom Expectations
Commitment to Diversity
I expect all students to be respectful of the widely varied experiences and backgrounds represented by the classroom members as a group. Disrespect or discrimination on any basis will not be tolerated. Whether inside or outside the classroom, if you encounter sexual harassment, sexual violence, or discrimination based on race, color, religion, age, national origin, ancestry, sex, sexual orientation, gender identity/expression, or disability, you are encouraged to report it to Gwen Lexow, Director of Title IX and Civil Rights Compliance at Bates at glexow@bates.edu or 207-786-6445. Additionally, please remember that Bates faculty are concerned about your well-being and development, and we are available to discuss any concerns you have. Students should be aware that faculty are legally obligated to share disclosures of sexual violence, sexual harassment, relationship violence, and stalking with the college’s Title IX Officer to help ensure that your safety and welfare are being addressed.
Academic Integrity
Please remind yourself of the Bates College policy on academic integrity. Please read this guide and its definitions of plagiarism, use/misuse of sources, and cheating. Students’ work will be closely scrutinized for plagiarism and violations of the College policy will not be tolerated. If you are concerned that your collaboration might put you at risk of an academic integrity violation, please come see me during office hours as soon as possible.
Students with Learning Differences
If you have a condition or disability that creates difficulties with the assignments, please notify me as soon as possible. You will need to create documentation with the Office of the Dean of Students, so if you need accommodation, please do this as soon as possible.
Collaboration:
Collaboration is the basis of all scientific discovery and is often instrumental in the learning process. However, you are individually responsible for learning the course content. I encourage students to form study groups. However, if you are working together on homework or the final exam, then you must give written credit to the collaborators. The final written product must be your own. In other words, you may conceptually discuss the approach with your group, but you must write your code on your own. As there are many valid approaches to coding the same solution, acts of co-coding are easy to identify and will be treated as violations of academic integrity.
Late work:
For all of our deadlines, if you turn in a component late, you will lose 10% of the total score per day. For example, the maximum possible percentage for a product turned in one day late is 90. This policy does not apply to a documented personal or family emergency.
Emergencies:
If I must cancel class due to weather or an emergency, I will inform you via the class email list. Please consider your Bates email to be the default place to look for class-related information and get into the habit of checking it daily.
Electronics:
Please silence your cell phone upon entering class and refrain from using it during class. When we are not working with R, this is laptop-free classroom. There are good reasons for this: laptop use is correlated with lower learning outcomes for you and those around you, and the act of taking notes on the laptop is less effective than hand-written notes. The only exceptions are those with documented accommodations from the Dean of Students.
Email:
Emails sent to me between 7:00am and 6:00pm will receive a same-day response. I will do my best to respond all emails within 24 hours of receiving, but may be slower for those sent late at night, as well as during weekends and breasks.
Helpful Resources
Office hours! I really want to set you up for success in this course, and would love to chat!
Attached tutor: Wuyue Zhou wzhou@bates.edu
Math and Stats Workshop. Located in the basement of Ladd Library, the workshop has drop-in hours, dedicated hours, and other options to help you succeed in this course!
You!
“Self-belief does not necessarily ensure success, but self-disbelief assuredly spawns failure.” ~ Albert Bandura
Grading
Grading Overview
| Product | Grade Percentage |
|---|---|
| Pre-class quizzes | 30% |
| Homework | 40% |
| Take-home final | 30% |
Pre-class quizzes: 30% of total
Before each class, you will complete a short concept quiz on Lyceum. The quiz will test your familiarity with the content presented in the readings, will open 24 hours in advance of the class, and will close at 9am on the day of class so that I can review performance. These will be graded on a binary scale. Your lowest two grades will be dropped, and no make up is available for quizzes.
Homework: 40% of total
At the end of each class, there will be a short homework assignment that will be due at 9am on the day of the next class session. The nature of the homework will vary, but may consist of short scripting exercises in R, 1-3 applied problems, or a written paragraph that explains a particular concept. These will be graded on a 0-3 scale as follows: 0: Absent; 1: Major errors; 2: Good (modal grade); 3: Exceptionally good answer. (You should consider 3 to be extra credit). For each homework assignment, 25% of the class will be sampled at random for grading with detailed comments. Your lowest two grades will be dropped, and no make up is available for homework.
Take-home final: 30% of total
On the last day of class (December 4), the final exam will be released. You will have until the end the scheduled final exam (December 13 5:45pm) to turn it in on Lyceum. The format of the exam is a statistical consultation. You will receive a dataset and two or more decisions that could be taken by your client. Your job will be to examine and analyze the data and to use your findings to advise your client on the best course of action. Grading rubric and additional information will be made available closer to exam time.
Your final grade will be determined on the following scale:
| Grade | Percentage | Grade | Percentage | |
|---|---|---|---|---|
| A+ | >95% | B- | 71-74% | |
| A | 87-94% | C+ | 67-70% | |
| A- | 83-86% | C | 63-66% | |
| B+ | 79-82% | D | 50-62% | |
| B | 75-78% | F | <50 |
Reading
We will be using selections from the following (free) open textbooks:
Danielle Navarro (2019) Learning Statistics with R (Abbreviated LSR)
Russell A. Poldrack (2018) Statistical Thinking for the 21st Century (Abbreviated ST)
David M. Diez, Christopher D. Barr & Mine Cetinkaya-Rundel. (2014) Introductory Statistics with Randomization and Simulation (Abbreviated OIS)
If you would like a copy of this source in print, there are very affordable ($8.49) copies on Amazon.
David Lane (n.d.) Online Statistics: An Interactive Multimedia Course of Study (Abbreviated OSB)
The reading schedule is on the course schedule. Readings that are covered on the quiz are under “Readings”, and readings that will enable you to work ahead are listed in “Read Ahead”.