What makes a good question?

Research Methods — Week 1

Welcome

Research Methods

This module is about learning to do research honestly.

Two strands, woven together:

1. Statistics — how to test claims with data
2. Communication — how to present evidence so it can be trusted

• [5 min] Welcome. Set the tone: warm, rigorous, honest.
• This module isn’t about memorising tests — it’s about developing habits of thought. By Week 10 you’ll have a toolkit of reflexive questions (“Is that a big number?”, “What assumptions are we making?”) that will serve you in dissertations and careers.
• Mention: two 2-hour sessions per week — content then application.

Questions?

Submit questions anonymously throughout the session:

PollEv.com/geol

or text geol to 07480 781235

Top-voted questions rise to the top — we’ll address them at natural break points.

• Open the Poll Everywhere Q&A activity before the session and leave it active throughout.
• Questions appear in your Poll Everywhere presenter view; upvoted questions rise to the top.
• Address during transitions or at the end of concept blocks, not mid-explanation.
• Reset (archive) the activity between sessions so questions don’t carry over from the previous week.

Two phases

Phase 1 (Weeks 1–4)

Scaffolded mini-project:

“Is UK biomass electricity carbon-neutral?”

Real data. Real ambiguity. Culminates in a peer-reviewed policy briefing — with a twist.

Phase 2 (Weeks 5–10)

Your investigation:

Choose a sustainable energy question. Design the study. Analyse the data. Write a 4-page policy report.

This is your summative assessment (30%).

• Phase 1 is formative — low stakes, high learning. Everyone works with the same data so you can compare notes.
• Phase 2 is summative — you choose the question. Same skills, your topic. Groups of 3–4.
• The “twist” in Phase 1: don’t reveal yet. Just say “there’s a twist in Week 4 — you’ll see.”

What you already know

You’ve done GEOL1151 — you can code in Python, use Jupyter, work with data.

This module uses R instead of Python.

Why? R is built for statistics. The syntax is different, but the ideas are identical. You’ve done pre-term preparation to bridge the gap.

• Reassure: if you’ve done the pre-term preparation, you’re ready. If you haven’t, the application session is your safety net.
• Don’t dwell on R vs Python — it’s a tool choice, not an identity.

The scientific method

🎓 Concept block 1

What is science?

• Not a body of facts
• A process for reducing uncertainty
• We never prove anything — we accumulate evidence

• [20 min block] This is the intellectual foundation for the course.
• Start here: science isn’t what you know, it’s how you decide what to believe. Every claim in this module will be held to this standard.

The cycle

flowchart LR
    A[Observation] --> B[Question]
    B --> C[Hypothesis]
    C --> D[Prediction]
    D --> E[Test]
    E --> F[Revise]
    F --> B
    style A fill:#2471a3,color:#fff
    style E fill:#c0392b,color:#fff

Observation → Question → Hypothesis → Prediction → Test → Revise

• Walk through each step with a concrete example. The biomass question will serve as our running case study.
• Emphasise the cycle — science doesn’t end at “test.” The revision step generates new questions.

Two directions of reasoning

Deductive

General → Specific

If plate tectonics is correct, then we predict matching fossils on separated continents.

We test the prediction.

Inductive

Specific → General

We observe matching fossils on separated continents. We infer the continents were once joined.

We build toward a theory.

• Both are legitimate but different. Deduction is stronger logically (if the premise is true, the conclusion must follow). Induction is how we actually discover most things.
• In practice, science uses both — we inductively form hypotheses, then deductively test them.

Wegener’s continental drift

• 1912: Alfred Wegener proposes continents move
• Right conclusion (they do move)
• Wrong mechanism (he proposed centrifugal force)
• Decades of resistance — then plate tectonics

The lesson: good science can survive a wrong mechanism. What matters is whether the evidence accumulates.

• Wegener was a meteorologist, not a geologist — the establishment resisted partly for that reason. Good reminder that the source of an idea doesn’t determine its validity.
• The mechanism was eventually supplied by Hess (seafloor spreading) and Wilson (plates).

Testable or not?

✏️ Exercise 1

Instructions

I’ll show you some statements.

For each one, decide: Is this a testable hypothesis?

Think for 30 seconds, then discuss with your neighbour.

• [10 min] Think-pair-share format. Show each statement, pause, then take a few responses.
• The biomass statement should produce disagreement — that’s the hook for the rest of the module.

“The Earth is approximately 4.5 billion years old”

Testable — and extensively tested (radiometric dating, meteorite ages, lunar samples). One of the best-constrained numbers in geoscience.

“Earthquakes are caused by divine punishment”

Not testable as stated — no observable prediction that distinguishes this from natural causes.

“Wind turbines cause headaches in nearby residents”

Testable — but tricky. How do you control for the nocebo effect? Awareness bias? The design of the study matters enormously.

• This foreshadows Week 5 (experimental design, confounding).
• Don’t resolve it — just flag the complexity.

“CO₂ emissions from burning biomass should be counted as zero”

Testable? It depends.

• What timescale?
• What system boundary?
• “Carbon-neutral” is partly a scientific claim and partly a policy choice.

This is the question that will drive the next four weeks.

• This statement should generate genuine disagreement. Some students will say “it depends on the trees regrowing” — good. Others may say “it’s a policy definition, not a scientific hypothesis” — also good.
• Don’t resolve it. Leave it open. Curiosity is the goal.

Falsifiability

🎓 Concept block 2

Popper’s insight

We can never prove a theory.

We can only fail to disprove it.

Every test that could have disproven our hypothesis but didn’t makes us a little more confident — but never certain.

• [20 min block] This is the philosophical core.
• Karl Popper (1934): the demarcation problem. What separates science from non-science? Falsifiability.
• You don’t need to go deep into philosophy of science. The practical lesson: seek evidence that could prove you wrong.

Confirmation bias

We naturally seek evidence for what we already believe.

This is not a character flaw — it’s how human cognition works.

Science is a set of tools for overcoming this tendency.

• Give an everyday example: you think it always rains on bank holidays. You remember the rainy ones, forget the sunny ones. That’s confirmation bias.
• In data analysis: if you only look for patterns that support your hypothesis, you will always find them. This is exactly what Week 4’s “traitor” exercise is about.

The Wason selection task

Each card has a letter on one side and a number on the other.

Rule: “If a card shows a vowel, then the other side has an even number.”

A

K

4

7

Which cards must you turn over to test the rule?

• [15 min including exercise] Let students think for 60 seconds, discuss in pairs, then poll via Poll Everywhere.
• Most students pick A and 4 (confirmation). The correct answer is A and 7 (seeking disconfirmation).

Cast your vote

PollEv.com/geol

text geol to 07480 781235

• Before the session: Create a multiple-choice poll with options A, K, 4, 7 (allow multiple selections). Activate it before showing this slide.
• Replace POLL_ID in the iframe src with the ID from your poll’s embed code (My Polls → Share → Embed).
• Safari caveat: On Apple devices, the iframe may not track cookies unless the student has previously visited pollev.com. The JS widget avoids this — if needed, replace the iframe with the <script> embed from Poll Everywhere’s “Web Embed” tab.
• Wait until responses plateau, then advance to the reveal.

Why A and 7?

A ✓

K

4

7 ✓

• A (vowel): if the other side is odd, the rule is broken
• 7 (odd): if the other side is a vowel, the rule is broken

• K (consonant): the rule says nothing about consonants
• 4 (even): whether the other side is a vowel or not, nothing is disproved

• The key: 4 feels relevant because it confirms the rule. But confirmation doesn’t test anything. Only disconfirmation does.
• Link forward: “Hypothesis testing in statistics is a formalised version of this — we ask: if the null hypothesis were true, would we see data this extreme? We’re seeking disconfirmation.”

Why is this hard?

Humans are wired to confirm, not disconfirm.

In this module, you’ll learn to:

• Ask “what evidence would change my mind?”
• Seek comparisons, not just supporting cases
• Treat uncertainty as information, not failure

• This connects to every subsequent week. Week 3: “what assumptions are we making?” Week 6: “how plausible was this before we tested?” Week 8: “what is the model not capturing?”
• The habits are the real learning outcome. The specific tests and methods are tools in service of these habits.

Meet R

🎓💻 Concept block 3

Why R?

You know Python (NumPy, Jupyter, pandas)

Same ideas. Different syntax.

R is built for statistics

• t.test(), lm(), aov() — one line each
• ggplot2 — the grammar of graphics
• Built-in data manipulation

You’ll see why this matters starting in Week 2.

• [15 min] This is NOT an R lesson. That was pre-work.
• The goal: show that R is working, connect it to what they know, and move on. Keep it to ≤10 minutes of live coding.

Live demo

🖥️ Switching to WebR

head(df) · summary(df) · plot(df$x, df$y)

• Switch to the WebR course page. Load a few rows of energy data.
• Show: head(df), summary(df), plot(year, biomass_twh).
• Narrate: “You already know how to do this. head() is like df.head() in pandas. plot() is like plt.plot(). The syntax is different but the logic is identical.”
• If something goes wrong (it will), normalise errors: “This happens to everyone. R’s error messages are actually quite helpful once you learn to read them.”
• ≤10 minutes. Then back to slides.

The biomass question

✏️💬 Integrative exercise

The setup

The UK burns millions of tonnes of imported wood pellets for electricity.

Most come from forests in the southeastern United States.

Under international carbon accounting rules, the CO₂ released at the chimney is counted as zero.

Is that reasonable?

Work in pairs. You have 10 minutes.

1. Write one testable hypothesis about biomass energy
2. What data would you need to test it?
3. What evidence would disprove your hypothesis?

• [20 min] Circulate and listen. Collect a few examples on the board / shared doc.
• Good hypotheses might be: “Biomass electricity produces less CO₂ per kWh than coal” (testable, but depends on system boundary). “Transporting pellets from the US adds significant emissions” (testable with import data + transport factors).
• The disprove question is the hard one — and the most valuable. It forces students to think like Popper.

What you came up with

Collect 3–4 examples from the room.

• Highlight: these are the questions that will drive the next four weeks. You’ll get the data in the application session.
• If anyone says “it depends on the timescale” or “it depends on what you include” — celebrate this. That’s exactly right, and it’s what Week 3 is about.
• End the session with the question feeling genuinely open. Resist giving the punchline.

Wrap-up

Key points

1. Science is a process for reducing uncertainty, not a collection of facts
2. We test hypotheses by seeking disconfirmation, not confirmation
3. The biomass question — “Is it carbon-neutral?” — is genuinely open, and the answer depends on choices you’ll learn to identify

Next time

Application session: “Meet the data (and Git)”

You’ll:

• Load real UK energy data in WebR
• Make your first plots
• Set up your GitHub repo and make your first commit

If you haven’t done the pre-term preparation, do it before the next session.

• End on a practical note. The application session is where the abstract ideas become concrete.
• Students who haven’t done the pre-work should not panic — the application session will support them — but they should try to catch up.