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Rindskopf’s Rules for Statistical Consulting

Our statistical consulting mini-symposium yesterday was great. I wish we’d been able to video it. There was lively discussion of the connections between statistical consulting and research, and the different aspects of consulting in academic, corporate, and legal environments.

I’ll be posting everyone’s slides. Here’s David Rindskopf‘s contribution:

Rindskopf’s Rules for Statistical Consulting

Some of these rules are universal, while others apply only in particular situations: Informal academic consulting, formal academic consulting, or professional consulting. Hopefully the context will be apparent for each rule.

Communication with the Client:

(1) In the beginning, mostly (i) listen and (ii) ask questions that guide the discussion.

(2) Your biggest task is to get the client to discuss the research aims clearly; next is design, then measurement, and finally statistical analysis.

(3) Don’t give recommendations until you know what the problem is. Premature evaluation of a consulting situation is a nasty disease with unpleasant consequences.

(4) Don’t believe the client about what the problem is. Example: If the client starts by asking “How do I do a Hotelling’s T?” (or any other procedure), never believe (without strong evidence) that he/she really needs to do a Hotelling’s T.

Exception: If a person stops you in the hall and says “Have you got a minute?” and asks how to do Hotelling’s T, tell them and hope they’ll go away quickly and not be able to find you later. (I’ve had this happen, and if I ask enough questions I inevitably find that it’s the wrong test, answers the wrong question, and is for the wrong type of data.)

Adapting to the Client and His/Her Field

(5) Assess the client’s level of knowledge of measurement, research design, and statistics, and talk at an appropriate level. Make adjustments as you gain more information about your client.

(6) Sometimes the “best” or “right” statistical procedure isn’t really the best for a particular situation. The client may not be able to do a complicated analysis, or understand and write up the results correctly. Journals may reject papers with newer methods (I know it’s hard to believe, but it happens in many substantive journals). In these cases you have to be prepared to do more “traditional” analyses, or use methods that closely approximate the “right” ones. (Turning lemons into lemonade: Use this as an opportunity to write a tutorial for the best journal in their field. The next study can then use this method.) A similar perspective is represented in the report of the APA Task Force on Statistical Significance; see their report: Wilkinson, L., & APA Task Force on Statistical Inference. (1999). Statistical methods in psychology journals: Guidelines and explanations. American Psychologist, 54, 594-604.

Professionalism (and self-protection)

(7) If you MUST do the right (complicated) analysis, be prepared to do it, write a few tutorial paragraphs on it for the journal (and the client), and write up the results section.

(8) Your goal is to solve your client’s problems, not to criticize. You can gently note issues that might prevent you from giving as complete a solution as desired. Corollary: Your purpose is NOT to show how brilliant you are; keep your ego in check.

Time Estimation, Charging for Your Time, etc.

(9) If a person stops you in the hall and asks if you have a minute, make him/her stand on one leg while asking the question and listening to your answer. If they ask for five minutes, it’s really a half-hour they need (or more).

(10) Corollary: Don’t charge by the job unless you really know what you’re doing or are really desperate. Not only do people (including you) underestimate how long it will take, but (a la Parkinson’s Law) the job will expand to include everything that comes into the client’s mind as the job progresses. If you think you know enough, write down all of the tasks, estimate how much time each will take, and double it. Also let the client know that if they make changes they’ll pay extra (Examples: “Whoops, I left out some data; can you redo the analyses?”, or “Let’s try a crosstab by astrological sign, and favorite lotto number, and…”)

(11) Charge annoying people a higher hourly rate. If you don’t want to work for them at all, charge them twice your usual rate to discourage them from hiring you (at least if they do hire you, you’ll be rewarded well.)

Resources ASA section on consulting Their guide to books and journals on statistics

Boen, J.R. and Zahn, D.A. (1982) The Human Side of Statistical Consulting, Lifetime Learning Publications.

Javier Cabrera and Andrew McDougall. (2002). Statistical Consulting. Springer-Verlag.

Janice Derr. (2000). Statistical Consulting: A Guide to Effective Communication.. Pacific Grove CA: Duxbury Press, 200 pages, ISBN:0-534-36228-1.

Christopher Chatfield (1988). Problem solving: A statistician’s guide, Chapman & Hall.

Taplin R.H. (2003). Teaching statistical consulting before statistical methodology. Australian & New Zealand Journal of Statistics, Volume 45, Number 2, June 2003, 141-152. Contains a good reference list on statistical consulting.


  1. John Chandler-Pepeln says:

    Another reference I've found useful is

    Gerald van Belle (2002). Statistical Rules of Thumb.

    Chapter 8 is on consulting and is very much in-line with Dr. Rindskopf's advice.

    P.S. I also like the "Doorknob Corollary": any questions or comments made while the client's hand is actually on the doorknob leaving the meeting will invariably be the most important/difficult piece of the analysis.

  2. Jean-Luc says:

    "Don’t believe the client about what the problem is."

    LOL, this is a general rule for all kind of consulting!

  3. C. Zorn says:

    Very useful. It strikes me that a number of these rules are also good advice for when a fifth-year Ph.D. student you've never seen before shows up in your office and says "I was wondering if I could ask you for some help with my dissertation…"

  4. Rindskopf makes a lot of good points here. Especially in the beginning, communication is the most important task as you're developing a sense of the scope of the project and your client's research questions. The collaborative aspect is what I enjoy most about consulting.

    David Kremelberg
    DK Statistical Consulting