Module 20 Activity Worksheet

Module: Module 20: Statistical Models and Inference for Connectomics
Duration: 4-6 hours
Generated from the module page. Edit modules/module20.md, not this file.

Capability target

Design and execute a connectomics inference plan that includes null-model choice, multiplicity control, uncertainty reporting, and explicit claim boundaries.

You are done when you can demonstrate this, not when you have filled in every box below.

Before you start

Check that you have:

• Basic probability/statistics
• Graph representation concepts

Bring one question you already have about this topic. Write it here so you can check at the end whether it was answered:

My question:

Questions this module answers

Keep these in view. At the end, answer each in one sentence.

1. Which null model is valid for this connectome hypothesis?
   • Your answer:
2. How should multiplicity be handled across motif families?
   • Your answer:
3. What claims are robust versus exploratory?
   • Your answer:

The task

Scenario: A team reports motif enrichment in one dataset and asks whether the claim generalizes.

1. Propose at least two candidate null models and justify each.
2. Run or outline multiplicity-aware testing strategy across motif set.
3. Draft a results summary separating exploratory and confirmatory findings.
4. Add one robustness check for cross-dataset comparability.

What you hand in

• Inference design sheet (estimand, null, tests, correction)
• One-page claim calibration summary
• Robustness plan with pass/fail criteria

Working checklist

Tick as you go. If you skip a step, write why — a skipped step with a stated reason is a decision; a skipped step without one is a gap.

• Question-to-test mapping
• Null-model design
• Inference execution
• Robustness checks
• Claim calibration

Evidence and reasoning

Fill one row per claim you make in your artifact. A claim without a limitation is not finished.

Confidence. For your main claim, mark one and say why:

• High — two or more independent lines of evidence agree
• Medium — one strong line, or several that share a weakness
• Uncertain — the deciding evidence is not available to me

Why:

One alternative I considered and rejected, and the reason:

Misconception self-check

These are the errors this module is designed to prevent. Confirm you did not make them, or note where you nearly did:

• I did not assume: A generic random graph is an adequate null for a connectome.
• I did not assume: A small p-value speaks for itself, regardless of how many tests were run.
• I did not assume: A hypothesis found in the data can be confirmed by the same data.

Session timing (facilitator reference)

Rubric

Score yourself before anyone else does. Where you fall short, name the specific next action rather than a general intention.

• Minimum pass
  • Null model is justified and the constraints it preserves are listed explicitly, in terms of what the hypothesis treats as uninteresting.
  • Total test count — including tests run and not reported — is documented, and a named correction is applied against it.
  • Claims are partitioned into exploratory and confirmatory blocks with different language in each.
• Strong performance
  • Sensitivity analysis spans at least two preprocessing choices (synapse threshold, inclusion criteria), with results reported for each variant.
  • Effect sizes with uncertainty intervals appear alongside every significance statement.
  • Error-sensitivity band computed at measured merge and split rates, with the direction of merge bias named.
  • Generalization boundary stated: which dataset, version, and region the claim covers, and what it says nothing about.
• Common failure modes
  • Null model choice disconnected from the biological question.
  • Selective reporting: significant outcomes shown, the full test count uncounted.
  • Exploratory signal conflated with validated inference.
  • Analytic p-values used where dependence between tests calls for permutation.

My self-assessment:

• Strongest part of my work, and the evidence for that:
• Weakest part, and the specific next action:

Exit prompt

Write a 6-8 sentence inference note that includes:

1. hypothesis and estimand,
2. null-model assumptions,
3. multiplicity strategy,
4. one robust conclusion and one unresolved uncertainty.

Your answer:

Peer review (swap worksheets)

Reviewing someone else’s reasoning is the fastest way to see the gaps in your own. Assess the evidence quality, not whether you agree with the conclusion.

• Is every claim paired with specific evidence?
• Is at least one limitation stated, and is it a real one?
• Is the confidence level justified by the number of independent evidence lines?
• One thing this person did better than me:
• One question I would ask them:

Module page: /modules/module20/ · Slides: /modules/slides/module20/ · Facilitator guide