First tests

Research Methods — Week 6 Application

Today’s plan

Goals

You have a research plan and a hypothesis. Today: test it.

1. Load and clean your project data
2. Visualise before you test
3. Check assumptions
4. Run the t-test
5. Interpret — honestly

• [10 min] Setup: pull latest changes from group repo. Recap.

Questions?

Submit questions anonymously:

PollEv.com/geol

text geol to 07480 781235

• 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.

t-test workflow

🎓💻 Guided example

The complete workflow

🖥️ Live demo on example data

1. Load data
2. Visualise: histograms, boxplots
3. Check assumptions: shapiro.test(), QQ plot
4. Run: t.test(y ~ group, data = df)
5. Interpret: CI, effect size, plain English
6. If violated: log-transform or wilcox.test()

• [20 min] Work through the full pipeline on neutral example data. Emphasise: always visualise before you test.

The golden rule

Always visualise before you test.

If you haven’t seen the data, you can’t interpret the result.

Group work

✏️💻 Apply to your data

Your workflow

1. Load your project data. Make sure it’s clean.
2. Define your groups / comparison.
3. Visualise. What does it look like?
4. Check assumptions.
5. Run the t-test.
6. Interpret.
7. Write up a paragraph of results.

Finished early? Try a second comparison or explore subgroups.

• [50 min] Groups work on their data. Circulate with demonstrators.
• Many groups will spend most of this on data cleaning. That’s realistic — say so: “In real research, 80% of the time is data preparation.”
• If a group’s data don’t suit a t-test (more than 2 groups, categorical variable), help them preview Week 7’s ANOVA.

Discussion

💬 What did you find?

Share your results

2–3 groups share:

• What was the hypothesis?
• What was the p-value?
• What was the effect size?
• Do you believe the result?

• [15 min] For each group, model the conditional-thinking habit: “How plausible was this before you tested? Does the p-value change your mind a little or a lot?”

The question to always ask

“How plausible was this before we tested?”

Does the p-value change your mind a little — or a lot?

Wrap-up

Commit your work

Commit analysis code and written results to your group repo via PR.

Next week: “What if you have more than two groups?”

ANOVA, multiple comparisons, and effect sizes.

• Encourage intermediate commits, even rough code. The commit history is part of the learning.