"There are three types of lies: lies, damn lies and statistics."
~ Mark Twain (or Benjmain Disraeli)

"Statistics is NOT like riding a bicycle."
~ Anonymous Statistics Professor

Overview and Goals: This course will cover the basic statistical procedures used by social scientists - especially psychologists - including: Confidence Intervals, T-tests, Analysis of Variance (ANOVA), Correlation, as well as Single and Multiple Regression; if time allows, some non-parametric analysis techniques will be discussed. Because we are going to talk about analyzing data from experiments, we will also need to discuss issues of experimental design. As you will see, the way you design an experiment often dictates how you analyze the resulting data.

I must stress that the course will not be merely a “cookbook” of statistical procedures. We will also talk about statistical theory. By selecting one or another statistical procedure, a researcher either tacitly or knowingly embraces a set of assumptions about either the data that has been collected, or the world in general. Different people may make different assumptions regarding the same set of data and it is often difficult to determine which assumptions are the most appropriate. Unfortunately, different sets of assumptions and/or procedures often lead to different conclusions (this might help explain Twain’s feeling about statistics). Thus, it is important for students to understand the theoretical background that underlies the practice of statistics.

The goals of the course are several. At the most basic level, you will learn how to distill a cumbersome set of numbers into an easily understood form. That is, you will learn how to analyze a set of data and how to present your results to the scientific community. At a more general level, you will learn what kinds of work goes into collecting and analyzing data so that you can be an "educated consumer" when it comes to the vast amounts of information currently available via, television, radio and the internet.

Attendance: You must come to every scheduled class! This is not a joke. Statistics is unlike other classes you might take because what we learn in Chapter 1 will be important for Chapters 2, 3, 4, etc. The course is not composed of separate modules so you cannot skip a chapter in the hope that it won't be on the final. Come to class!!!

Course Readings: There is no required text for the course. A copy of Statistics for the Behavioral Sciences by Gravetter and Wallnau is available in the library. Some students find it helpful to read the text prior to class. I would encourage you to make use of this resource if you think it will help you. There will be a handful required readings available via e-Reserves. These readings are designed to help you connect what we do in this class with what you do in your other classes in the Psychology major. Specifically, we are going to 'decode' the Results section so that you will be able to read and understand results sections on your own moving forward.

Website: The course website includes a copy of the syllabus and course schedule. Problem sets (and their accompanying solutions) and materials for the Lab sessions will only be available via the website (Note: I will ask you to turn in your assignments electronically, as well). You can also download the slides that I use in video lectures and in class from the website. I strongly encourage you to download the slides as it will save you a lot of time needlessly copying down the details of lengthy word problems.

Video Lectures: You will be responsible for watching video lectures prior to class every week; class time will be used to review and reinforce what we cover in the video lectures. One of the main advantages of the video lectures is that you can watch them at your pace. You can pause/rewind the videos as needed. Links to all the videos can be found on the schedule page. It is VERY important that you watch the videos and work on the sample problems contained therein prior to class. I will start each class with the assumption that you have done this important prepatory work. Remember: practice is the key to success in this course!

Checks: One of the main disadvantages to using video lectures is that it makes it hard for me to know what you have and have not understood from the lectures (e.g., confused faces and whispered conversations are important feedback from in-person lectures). To help give me a little more feedback, I have written short checks to accompany each set of video lectures. These are short and required but not graded. You get full credit for completion even if you get everything wrong. Seeing your responses will help me determine what we need to cover in class, so please take these assignments seriously They are due by midnight on the night before class. Links to the check assignments can be found on thecan be found on the schedule page.

Quiz / Exams: There will be four exams. The first exam is woth 15% of your grade; the remaining three exams are worth 20% of your grade. All exams will be cumulative (based on all prior work for the semester) but will tend to focus on more recent work. The exams will be a combination of short-answer (a word or a few sentences) and problem-solving questions. Make-up exams will only be given in cases of documented illnesses or emergencies.

SPSS: During the course, we will learn to do many analyses the old-fashioned way, by hand. Learning the hand calculations is important because it allows you to see the *guts* of statistical procedures. However, given that virtually all statistical work is now done on computers, I think it is important to expose you to this process, as well. We are going to use a software package called SPSS. SPSS is a good package for students because it is menu-driven which makes it relatively easy to use. And Amherst has a license so you can download a copy of the software to your computer.

Problem Sets:Your homework grade will based on a number of components including but not limited to problem sets, video lecture "checks", lab "prep" questions, data collection assignments, and other assignments announced in class at the discretion of the instructor. Problem sets will be due approximately every other week. These assignments may include questions drawn from lab sessions. Some problem sets will require the use of SPSS. In general, homework assignments will be due on Friday afternoons at 5:00 PM (but check the schedule; there are a few exceptions) and will account for 10% of your grade. Homework assignments are posted on the website; you will not receive a paper copy of assignments (but you can print one off the website). Homeworks will be graded on a 10-point scale. Effort will be rewarded, so be sure to attempt every problem, even if you don't necessarily answer every question correctly.

To receive credit, you must show your work. Answers to the homework assignments will be posted on the course website at 5:00 PM on the due date. ANY assignment turned in after the deadline will receive a grade of 5, but keep in mind that turning in your assignment late will be much better than failing to hand it in at all.. You may consult with your fellow students regarding homework problems; however, each student must turn in their own work. Homeworks will be turne in via the Moodle site.

Final Project: You will complete a final project at the end of the semester that will allow you to bring together all of the statistical skills you developed this semester. More information about the final project will be made available later in the semester, but broadly speaking, you will be asked to conduct, interepret, and report the results of a variety of statistical analyses using data collected in class.

Office Hours: My office hours are listed at the top of the syllabus. If these times are not convenient, please come see me after class and we can schedule an appointment. You can also email me to set up an appointment, but that system often leads to round after round of "Email-tag". One of my favorite parts of this job is meeting with students so please stop by even if you don't have a major problem.

Final Grades: Your final grade will be determined, as follows:

The assignment of letter grades will be based on 2 factors: 1) how you perform in an absolute sense, and 2) how you perform relative to everyone else in the class. This is usually a little confusing to students, so let me try to explain. Let's say the first exam is based on 100 points and you score a 75. Is that good or bad? It depends on how the rest of the class did. If the mean score on the exam was 50, you did very well. If the mean score was 90, you did not do well. That's the relative part of your grade. The absolute part of the grade ensures that you won't get a bad grade just because the class in general aces an exam. Let's say you score a 90, but the class average is 95. Would you receive a poor grade? No, because you clearly mastered most of the material on the exam, and thus deserve a high mark. However, be advised, an exam where the mean score is 95% would not discriminate students who know the material from those who don't. Therefore, exams will be written so that it is unlikely that a majority of the class will score 90% or better.

Calculator: A decent calculator (app) - one that has Memory, Exponent and Square Root functions - will be extremely helpful. You cannot program information into your calculator for exams.

Formula Sheet: For each exam, you will be allowed to prepare a formula sheet written on one half-sheet of notebook paper. Your formula sheet may contain as many formulas and definitions as you like, but bear in mind the sheet will be much more helpful if it is legible and well organized. You may not include any sample problems on your sheet. You MUST turn the sheet in at the end of the exam, so make sure you put your name on the sheet.

Accomodations: If you have a documented disability that requires accommodations, you will need to register with Accessibility Services for coordination of your academic accommodations. You can reach them via email at accessibility@amherst.edu, or via phone at 413-542-2337. Once you have your accommodations in place, I will be glad to meet with you privately during my office hours or at another agreed upon time to discuss the best implementation of your accommodations.

Aside to Math-Phobic Students: I am not going to lie to you, this course does involve math. However, the math is not going to be any more complex than the basic four (add, subtract, multiple, divide) with a little exponent/square root thrown in for good measure. Please do not panic! Natural arithmetic ability will help in this course; however, you do have the ability to learn this material even if you are 'arithmetically-challenged'.

Statement on Academic Honesty: It is my expectation that you will conform to all college policies regarding academic honesty. Some assignments (e.g., homeworks) allow for collaboration. Other assignments (exams, final project) do not. Violations of academic honesty policies will be punished swiftly and severely.

Policy on video/audio recordings and sharing of online materials: You may not record any part of this class without written consent of the instructor. You also may not share online materials with anyone outside of this class without written consent of the instructor.

Artificial Intelligence: Because generative artificial intelligence is a relatively new technology, it is hard to set firm boundaries regarding the use of AI and other generative technology in the classroom. For the purposes of this course, the general rule is as follows: If you would not ask a person for a particular kind of help, you may not use AI for that function. For example, you would not ask a person to write a section of a paper for you. But, you might ask a person for help finding journal articles related to the topic of your paper. Therefore, you can use AI (or PsycInfo) for that purpose. You would ask a person to provide written summaries of articles that you might use for your paper or to identify quotes that might be of use to you. Therefore, you cannot use AI for those purposes. Similarly, you would not ask a person to complete a question on an exam, quiz, or problem set for you. Consequently, AI cannot be used to do that, either. But you might ask someone to remind you of the correct formula to use in a problem; AI could be consulted for this purpose, as well.

It would be impossible for me to list every scenario in which AI could be brought to bear in this class, (maybe Chat GPT could provide such a list…). So the above is meant to serve as guidance. You can always ask about appropriate use of AI if you have questions. And, you should always indicate how AI was used in the preparation of any academic work that you submit for this class. Failure to disclose the use of AI for an assignment will be considered an act of academic dishonesty that will be reported to the Office of Student Affairs. 

Course Schedule