"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

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!

Quiz / Exams: There will be four exams. The first exam is worth 15% of your grade; the remaining three exams are worth 20% of your grade. All exams will be cumulative (as mentioned previously, content later in the course presumes and understanding of content from earlier in the course) but will 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 problem sets and other assignments announced in class at the discretion of the instructor. Problem sets will be due every week; again, practice is the most successful strategy for learning statistical analysis. 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). 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. Turning in an assignment after the deadline will lower your grade. You will receive feedback on your homework; that is, errors will be identified and explained. You will also have an opportunity to check your work against the posted solutions.

The evaluation of homework will use a contract grading model. My expectation is that you will complete every assignment on time. A student who meets this expectation will not earn any credit simply for meeting this expectation. However, students who turn in assignments late or incomplete will lose credit towards their final grade. This grading system was designed to promote consistent practice at the expense of the pursuit of perfection.

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. My preference is to meet in preference, but there is an option to connect via Zoom, as well. f 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. 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:

Calculator: A decent calculator - 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. 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.

Generative AI Policy: You may not use generative AI for any purpose at any time in this class. There are two reasons for this policy. First, my values actively conflict with those of generative AI. I design assignments with the goal of helping you become better readers, writers, and thinkers. Along the path to those goals, my core values include independence, novelty, and depth of analysis. Generative AI has a different set of values. The technology seems to value efficiency (how quickly can it generate a response to a prompt) and probability (what is most likely to come next). Efficiency undermines independence and depth of analysis; probability undermines all three.

Second, research indicates that if you want a group to follow a policy or law, it is best to make that policy or law as simple to follow. I have tried more 'common sense' approaches to generative AI but was forced to abandon them because students could not easily determine what was and was not permissible; students also failed to disclose their use of generative AI tools.

Any suspected use of AI will be referred to the Student Affairs Office. Depending on the context and type of use, the penalty for violation of this policy may include a failing grade or a grade of zero on the assignment in question. Failure in the course is also a possibility, again, depending on the nature of the offense. Given the shifting nature of the AI space, I cannot provide a list of all possible programs and/or activities that might be considered a violation of this policy but given that the College has purchased and distributed Grammarly to the student body, I do want to comment on the use of this product. You are allowed to use the version of Grammarly covered by the College's license, but you may not use GrammarlyGo (which is not included in the College's license) for any work submitted for this class.