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Regression & Experimental Design · Statistical Consulting

Working with researchers

Reading34mLesson 1 of 3 in this module

Run a statistical consulting intake that pins down the question, the data and the deliverable.

Why this matters

Most statistical work at the mid-level is done with someone else: a chemist planning a stability study, a biologist with a plate of assays, a plant manager who wants to know why yields dropped, a product team running an A/B test. The technical skills in this course only help if you understand the real question, the real data and the real constraints. Statisticians and data scientists who are good at consulting get brought in early, when they can still shape the design; those who are not get handed a spreadsheet the week before a deadline.

What a consulting intake is for

The first meeting (the intake) has four goals:

  1. Understand the scientific or business question in the collaborator's own words.
  2. Understand how the data were or will be produced: units, treatments, measurements, timing.
  3. Agree on scope, deliverables, timeline and roles.
  4. Spot problems early: confounding, pseudo-replication, missing controls, a sample that is too small, ethical or data-access issues.

It is not the place to show off methods. Most of your time should be spent listening and asking questions.

A question list that works

The question

  • What decision will this analysis inform? What would you do differently if the answer were yes versus no?
  • What result would surprise you? What do you already believe?
  • Is the goal to estimate an effect, compare options, predict, or explain?

The design and data

  • What is the experimental (or observational) unit? How many are there?
  • How were treatments assigned? Was there randomisation, blocking, blinding?
  • Are there repeated measurements on the same unit? Batches, plates, days, sites?
  • What exactly is measured, with what instrument, and what is its precision? Are there detection limits?
  • What is missing, and why?

The constraints

  • Deadline, budget, number of runs still possible.
  • Regulatory or reporting requirements (a GMP environment, a clinical protocol, a journal's guidelines).
  • Data access, privacy and who owns the data.

The deliverable

  • Who is the audience: the scientist, a manager, a regulator, a journal?
  • What form: a short memo, a figure, a full report, code they can rerun?

Translating between languages

Collaborators speak their domain's language; you speak statistics. Restate the question in both, and check you agree:

Collaborator: "Does the new buffer work better?"
Restated:     "Among assays run on the same day, is mean enzyme activity with
               buffer B higher than with buffer A, and by how much? A difference
               of at least 5 units would change which buffer we buy."

The restated version names the unit, the comparison, the blocking factor and the smallest important difference, which you need for power and for interpreting the result.

Red flags to raise early

  • "We measured each sample three times, so n = 30" (with 10 samples): pseudo-replication.
  • "All the treated samples were run last week": treatment confounded with time.
  • "We'll see what's significant": no pre-specified hypothesis, high risk of false positives.
  • "Can you get p below 0.05?": raise it politely and firmly. Your job is a sound analysis, not a particular answer. The American Statistical Association's Ethical Guidelines for Statistical Practice (amstat.org) cover exactly this situation.

Scope and expectations

End the intake with a short written summary, sent the same day:

  • the question as agreed;
  • the data you will receive, in what format, by when;
  • the planned analysis (in plain words) and what it can and cannot conclude;
  • deliverables, dates and authorship or acknowledgement expectations;
  • open questions.

This one-page summary prevents most later disputes. Update it if the scope changes.

Careers and credentials

Consulting skills are central for statisticians, data scientists, operations research analysts and lab scientists who become the "stats person" in their group. Formal requirements vary: a master's degree is typical for statistician roles, and the ASA offers an optional PStat accreditation for experienced practitioners. INFORMS offers the optional CAP for analytics professionals. Check the current requirements with the official bodies (amstat.org, informs.org) rather than relying on summaries. Checked on 2026-10-23.

Common mistakes

  • Jumping to methods before understanding the question.
  • Accepting the data as given without asking how it was produced.
  • Promising a result (or a p-value) instead of an analysis.
  • Not writing anything down, so scope creeps and memories differ.
  • Using jargon the collaborator cannot check. If they cannot explain your conclusion to their boss, it will be misused.

Quick recap

  • The intake is for listening: the question, the data-generating process, the constraints, the deliverable.
  • Restate the question in measurable terms, including the smallest important difference.
  • Raise design problems early and ethically; send a written scope summary.

Try it

The Do tab has you run a practice intake with a friend, classmate or colleague (or a role-play script), using the question list above, and write the one-page summary.

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