Session Kit: Module 20: Statistical Models and Inference for Connectomics
Everything needed to run Module 20 as a taught session: prep, timing, materials, misconceptions, rubric.
Generated from modules/module20.md. Edit the module page, not this file.
At a glance
| Duration | 4-6 hours |
| Capability target | Design and execute a connectomics inference plan that includes null-model choice, multiplicity control, uncertainty reporting, and explicit claim boundaries. |
| Learners leave with | Inference design sheet (estimand, null, tests, correction) |
Before you walk in
- You can state the capability target in one sentence without reading it.
- You have one worked example you will narrate, including where you are unsure.
- Data access works — accounts, viewer, notebook — verified today, not last week.
- The rubric is visible to learners before they start, not after.
- You have decided what “uncertain” earns, and you will say so out loud.
Learners should arrive having covered:
- Basic probability/statistics
- Graph representation concepts
Materials
Run of show
| Time | Segment | Your note |
|---|---|---|
| 00:00-06:00 | Framing: the null is the scientific step | |
| 06:00-18:00 | Worked example: reciprocity across nulls | |
| 18:00-30:00 | Guided practice: write the uninteresting explanation | |
| 30:00-40:00 | Multiplicity | |
| 40:00-50:00 | Robustness and error sensitivity | |
| 50:00-57:00 | Competency check | |
| 57:00-60:00 | Exit ticket |
The activity
Scenario: A team reports motif enrichment in one dataset and asks whether the claim generalizes.
- Propose at least two candidate null models and justify each.
- Run or outline multiplicity-aware testing strategy across motif set.
- Draft a results summary separating exploratory and confirmatory findings.
- Add one robustness check for cross-dataset comparability.
What learners hand in
- Inference design sheet (estimand, null, tests, correction)
- One-page claim calibration summary
- Robustness plan with pass/fail criteria
Misconceptions to target
These are the errors this session exists to prevent. Surface them in the debrief rather than pre-empting them in the lecture — a misconception a learner has voiced is far easier to correct than one they are holding silently.
- They may believe: A generic random graph is rarely an adequate connectomics null.
- Surface it by asking: “What would have to be true for that to hold? What would change your mind?”
- They may believe: Reporting only p-values without multiplicity context is incomplete.
- Surface it by asking: “What would have to be true for that to hold? What would change your mind?”
- They may believe: Post-hoc storytelling is not confirmatory inference.
- Surface it by asking: “What would have to be true for that to hold? What would change your mind?”
Naming the norm
Every session is a chance to make one piece of the hidden curriculum explicit. Pick a moment where you would normally just do the professional thing, and say out loud why you are doing it — then ask whether anyone was taught that.
For this session, the candidate is whichever norm the activity most depends on: stating an assumption in the same sentence as the claim, recording the version a number came from, or saying “uncertain” and having it count as a real answer. See the hidden curriculum for the collected set and why naming them is a fairness intervention rather than etiquette.
Assessment
- Minimum pass
- Strong performance
- Common failure modes
Grade the reasoning, not the answer. A correct call with no evidence chain should not outscore a well-reasoned incorrect one — and saying so publicly changes behaviour within one session.
Exit prompt
Write a 6-8 sentence inference note that includes:
- hypothesis and estimand,
- null-model assumptions,
- multiplicity strategy,
- one robust conclusion and one unresolved uncertainty.
If this session goes wrong
- Nobody talks in the debrief. You asked “any questions?” Ask instead: “Which cue would you drop first if the data got worse?”
- Everyone finishes early. They are pattern-matching, not judging. Give an ambiguous case where the answer is “uncertain” and see what happens.
- Nobody finishes. The scaffolding came off too fast. Work the next case together rather than pressing on.
- A learner is silently lost. The most likely cause is unstated vocabulary. Point them at the dictionary and check back.