Course: Introduction to Psychology (PSYC 1) · Silver Oak University (fictional sample) · Prof. Bennett
Module: Week 2 of 16 · Fall 2026 · in-person, two 75-minute sessions
Objective covered: Objective 2 — Evaluate psychological research methods and ethics, distinguishing correlation from causation and identifying sources of bias.

This file holds two pieces: (A) the Module 2 Overview page ("Start Here") and (B) the Welcome Announcement that drips out when the module opens. Dates below assume a Tuesday/Thursday session pattern with Week 2 meeting Tue Sep 8 and Thu Sep 10, and end-of-week work due Sunday Sep 13, 11:59 p.m. (Note: Mon Sep 7 is Labor Day — campus closed.) Adjust the day-of-week and times to match your section.

(A) Module 2 Overview — Start Here

Welcome to Week 2: Research Methods & Ethics

This is your home base for the week. Read it first, then work the checklist below from top to bottom. Everything you need is linked inside the module.

Last week we made a promise: psychology trusts evidence over intuition. This week is the fine print on the word evidence. Every time you see "studies show…," somebody made choices about how they studied it — and those choices decide what the study is actually allowed to claim. By Friday you'll be able to look at any research headline and ask the questions that separate a real finding from a confident-sounding overreach: What kind of study was this? What can it honestly conclude? And is anyone allowed to say one thing caused another?

The week's big question

"How do psychologists actually know what they claim — and why does a link between two things almost never prove that one causes the other?"

By Friday you'll be able to name the three research designs, explain why correlation isn't causation, tell random sampling from random assignment, and list the ethical rules every study must follow.

By the end of this week, you can…

Use this as a checklist. If you can do all four out loud, you're ready for the quiz.

• [ ] Walk the scientific method — theory → testable hypothesis → operational definition → collect data → replicate — and say why each step matters.
• [ ] Tell apart the three designs — descriptive (case study, naturalistic observation, survey), correlational, and experimental (IV vs. DV, experimental vs. control group, random assignment) — and state what each can and can't claim.
• [ ] Explain correlation ≠ causation — the third-variable problem and the directionality problem — and why only an experiment supports a cause; read a correlation coefficient (−1 to +1: sign = direction, size = strength).
• [ ] Distinguish random sampling from random assignment, and name the core research ethics: informed consent, right to withdraw, protection from harm, justified deception + debriefing, confidentiality, and IRB review.

What's due this week, and when

Work these in order — each one gets you ready for the next.

Heads-up on the AI tutorial: you'll use a chatbot to draft and explain, and then you judge its work against what we cover in class. Chatbots are notorious for sliding from a correlation straight to a cause — they'll tell you a study "proves" one thing causes another when all it found was a link. Catching that slip is the point.

Late policy reminder: 10% off per day late. If life happens, reach out before the deadline — I'd much rather hear from you early.

How to succeed this week

• Lead with the idea, not the jargon. "Independent variable" is just the thing the researcher changes; "operational definition" is just how you measured a fuzzy word in numbers. The vocabulary comes after the idea clicks.
• Memorize two tiny hooks. "Describe = watch · Correlate = link · Experiment = cause." And: "Sampling = who's studied (generalize); Assignment = who's treated (cause)."
• Practice the three-question move. For any "studies show" claim: What design? What can it conclude? What's a possible third variable? Run it on a real headline this week.
• Remember the headline lesson: a link is not a cause. Two reasons — a third variable could drive both, and a correlation can't tell you the direction. Only a randomized experiment earns the word "cause."
• Treat the chatbot as a smart intern, not an oracle. It drafts; you check whether it just upgraded a correlation into a cause. That habit is the whole semester in miniature.

You don't need any math for this week — this is about study design and ethics, not computation (that's the statistics course's job). Just bring your skepticism and a "studies show" headline you've seen lately. See you Tuesday.

(B) Welcome Announcement — Module 2

Release setting: post on the module's start day (offset = 0 days), i.e., Mon Sep 7, 2026 (or first thing Tue Sep 8 if you prefer to avoid the holiday) — not before. If your platform won't preserve the scheduled date on import, post this as a draft labeled "Release: Week 2 start."

Subject: Welcome to Week 2 — does the study app really cause better grades? 📊

Hi everyone — great work launching Week 1!

Quick warm-up: "Studies show students who use a study app earn higher grades." So… should you download it? Maybe — but here's the catch. What if the students who choose the app are simply the more motivated ones? Then the app didn't raise anyone's grades; motivation did, and the app just rode along. That gap — between two things being linked and one causing the other — is the single most important idea in this entire week, and it's the most expensive mistake in all of research.

This week — Research Methods & Ethics — we tackle the big question: How do psychologists actually know what they claim, and why does a link between two things almost never prove that one causes the other? By Friday you'll name the three research designs, explain why correlation isn't causation, tell random sampling from random assignment, and list the ethical rules every study must clear.

Three things not to miss:
1. Lecture Tutorial 2 — work through the week's ideas with one approved chatbot (Gemini, Claude, or ChatGPT) and submit the share link. You'll catch the model when it slides from "linked" to "caused," not just trust it. Due Sun Sep 13.
2. Quiz 2, Discussion 2, and Assignment 2 also close Sun Sep 13 — the discussion is a quick AI dialogue you summarize and post, so start early and leave time to reply to classmates.
3. Open the Start Here page first — it lays out everything in order with due dates. (Heads-up: Mon Sep 7 is Labor Day — campus closed.)

One promise: no math this week. This is about reading a study honestly — what its design lets it claim — not about crunching numbers (that's the statistics course's job). By Friday, the next time a headline says one thing "causes" another, you'll know exactly what to ask before you believe it.

Bring a "studies show" headline you've seen lately to class on Tuesday — we'll put a few on trial.

See you soon,
Prof. Bennett