Session Kit: Module 08: Hypothesis Testing in Connectomics
Everything needed to run Module 08 as a taught session: prep, timing, materials, misconceptions, rubric.
Generated from modules/module08.md. Edit the module page, not this file.
At a glance
| Duration | 4 hours |
| Capability target | Design one hypothesis test with metric, null model, and interpretation boundary statement. |
| Learners leave with | 3 hypothesis sheets (hypothesis, metric, null, interpretation boundary, non-claim) |
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:
- Modules 01-07
Materials
Run of show
| Time | Segment | Your note |
|---|---|---|
| 00:00-08:00 | Framing: good vs bad hypotheses | |
| 08:00-20:00 | Hypothesis drafting | |
| 20:00-34:00 | Metric and null model selection | |
| 34:00-46:00 | Interpretation workshop | |
| 46:00-60:00 | Competency check |
The activity
Scenario: Your lab is planning a study of feedforward vs feedback connectivity in mouse visual cortex using the MICrONS dataset. You need to design three testable hypotheses about the circuit architecture.
- Draft 3 hypotheses (one about feedforward connections, one about feedback connections, one about reciprocal connections).
- For each: specify the metric, null model, required dataset version, and analysis code outline.
- For each: write the supported claim and explicit non-claim.
- Exchange with a partner. Critique their null model choices and interpretation boundaries.
- Revise based on peer feedback.
What learners hand in
- 3 hypothesis sheets (hypothesis, metric, null, interpretation boundary, non-claim)
- Peer critique notes (minimum 2 substantive comments per hypothesis)
- Revised hypotheses incorporating feedback
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 significant result against a random-graph null is evidence of biological structure.
- Surface it by asking: “What would have to be true for that to hold? What would change your mind?”
- They may believe: The statistical test is the scientific step, when the choice of null model is.
- Surface it by asking: “What would have to be true for that to hold? What would change your mind?”
- They may believe: A metric can be chosen after seeing the data without cost to the inference.
- Surface it by asking: “What would have to be true for that to hold? What would change your mind?”
- They may believe: Reporting the tests that worked is sufficient without reporting how many were run.
- 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: Coherent hypothesis/metric/null trio for at least 2 of 3 hypotheses.
- Strong performance: Clear uncertainty and non-claim statements. Null model choice justified. Peer critique identifies genuine issues.
- Common failure to flag: Vague hypothesis without measurable endpoint (“we will study connectivity patterns”) or missing null model.
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 one claim and one explicit non-claim from the same test outcome.
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.