American Society for Quality

Some General Remarks on Consulting in StatisticsAuthor(s): Cuthbert DanielSource: Technometrics, Vol. 11, No. 2 (May, 1969), pp. 241-245Published by: American Statistical Association and American Society for QualityStable URL: http://www.jstor.org/stable/1267257 .

Accessed: 21/06/2014 00:14

Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at .http://www.jstor.org/page/info/about/policies/terms.jsp

.JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range ofcontent in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new formsof scholarship. For more information about JSTOR, please contact support@jstor.org.

.

American Statistical Association and American Society for Quality are collaborating with JSTOR to digitize,preserve and extend access to Technometrics.

http://www.jstor.org

This content downloaded from 188.72.126.182 on Sat, 21 Jun 2014 00:14:39 AMAll use subject to JSTOR Terms and Conditions

VOL. 11, No. 2

Some General Remarks on Consulting in Statistics*

CUTHBERT DANIEL

Statistical Consultant

I must apologize, first, for the pretentiousness of my title. I will, of course, restrict myself to my consulting. Quite a large part of my own work is only called statistics by my friends.

Every successful client-consultant relation seems to me to require three client conditions and one consultant condition. There must be a good problem, a ready client, and a favorable organizational situation. The consultant must be well prepared. It only remains to define or at least to comment on all these adjectives.

A good problem is, first of all, one that is important to the client. Do not work, or at least try not to work, on tiny unimportant problems, even as test cases. If you make a major contribution to the solution of a small problem, you have not made much of a contribution.

A good problem is one that is already clearly formulated when the consultant arrives, or that can be clearly formulated in short order with the consultant's help. (The consultant's aid on this matter may later be judged to be his major contribution.)

If, after one or two days' study, the prospects for a clear formulation seem poor, the consultant should limit his aims and indicate strongly to those in charge that he will probably not be able to make a major contribution.

To take the opposite contingency, it does happen that the problem is one with which the consultant is so familiar that he knows how to go ahead even though the client does not. Such situations require that care be taken not to damage the reputations of some of the working team. This is especially important when such damage is viewed with equanimity by others in the group.

A good problem is not necessarily an easy one. The analysis of a large mass of multi-factor data is rarely easy. But careful study will often produce information of a quality unattainable without statistical help. If the problem is an easy one, you will increase your credit by solving it quickly and leaving. Never draw it out, especially if you are on a per diem basis.

A good problem, semifinally, is one in the consultant's area of competence. You will learn as much as the client in the course of any sizable campaign, but you must have a wide margin at the start. It is crucial that you know your limitations and that you make them known to your potential client. Perhaps you will not need to carry this principle as far as I have had to, since most of

Received Sept. 1968. * This paper was read at the August 1968 ASA National meetings.

241

MAY, 1969 TECHNOMETRICS

This content downloaded from 188.72.126.182 on Sat, 21 Jun 2014 00:14:39 AMAll use subject to JSTOR Terms and Conditions

you will have had some formal statistical education. I have given up my earlier practice of presenting clients with a list of the parts of statistics about which I know nothing. The list was getting too long. And it concealed the fact that there were many topics not on the list only because I hadn't heard of them.

It can happen that a problem judged good early on, turns out to be more com- plex, even messier, than was foreseen. It may happen, and I hope often does in your case, that you can by taking thought extend current techniques to the new situation. Some problems require more time, or possibly more mathematical competence or ingenuity than is instantly at your disposal. There are many able mathematical statisticians in the world and some of them are glad to help. You can hardly count on being as lucky as I have in this respect, but another part of the full definition of a good problem is about to appear.

A good problem, while in the main familiar to the consultant, may open new research domains to mathematical statisticians. Some will work for love, and will mention you in their papers. Some will work on a fee basis (with the client's permission). Some will even produce results that are immediately usable. But in all cases you will be nudging mathematical statistics in the direction that most applied statisticians think it should go.

In summary, a good problem is:

a. important to the client, not minor or peripheral, b. clearly formulated or clearly formulable with consultant's help, c. not an easy one, but requiring expert aid, d. in the consultant's area of competence, e. likely to lead to new research, either by the consultant or by his academic

colleagues.

I have said that one requires ready clients as well as good problems. It is not so easy to define readiness, but if a considerable degree of psychological readiness is not at hand, a good problem may not get a good solution. There seem to me to be several types of client who are in this sense ready.

Those who have heard of you from someone you have already helped are prob- ably the most ready. Those who are desperate are often ready. (They've spent 450 out of the 500 thousand dollars allocated; they have only six months to go; so far they have not got any usable data.) Such cases must be studied carefully and accepted with explicit reservations. They may only want you as a fall guy.

It must be clear that by ready I do not at all mean docile or malleable, but rather responsive, instructable, willing, and able. There are surely many aspects of unreadiness. Those that I am sensitive to are:

1. PREJUDICE. This may be induced by contact with a less than competent statistician, or by conviction that all statistical methods are by nature opposed to scientific methods. (Some are, in my opinion.)

2. PREOCCUPATION. Most research and development men are somewhat

overextended with their current work and may react to the addition of a new point of view as to a last straw.

3. PRESTIGE. The addition of a complete outsider, who knows literally nothing

242 CUTHBERT DANIEL

This content downloaded from 188.72.126.182 on Sat, 21 Jun 2014 00:14:39 AMAll use subject to JSTOR Terms and Conditions

CONSULTING IN STATISTICS

about the subject-matter under study, may appear to the research worker as an expression of lack of faith by management.

Although I shall say a few unkind words later about "short courses" in sta- tistics (especially in multiple regression and in design of experiments), I believe that sometimes they do inspire an intelligent man, or woman. By an intelligent man I mean one who knows that he cannot himself carry through at a profes- sional level all the consequences of the methods he has heard proposed. Such a research worker will occasionally be able to produce a group who are ready.

The third desideratum is that the organizational situation be favorable. It is not easy-sometimes not even possible-to be objective about this at the beginning, but a few items of experience can be mentioned.

It might be thought that initiation from the very top would always be best. But it would be naive to suppose that he who enters at the suggestion of the Vice President in charge of research cannot fail. Everything depends on how close the initiator-be he V. P., project leader, or bench worker-is to the actual problem and work required. In general, I find that prospects are (organization- ally) most favorable when the project leader is already in favor of calling in a statistician and does not need to be persuaded. But I have had the experience of being invited in by the President with good success. In that case he under- stood the whole experimental situation very well and had close rapport with the Director of Research.

A distinctly unfavorable situation occurs when the statistician is called in to help mediocre research workers who are in direct competition with some first- raters working on the same problem. This case must be carefully distinguished from that in which a "theoretical" group is working in competition with an "empirical" group. It is the quality of the competing groups that is decisive, not their theoretical-vs-practical orientation.

It may as well be admitted that first rate first-raters rarely need any statistics at all. So we are, in one sense, obligated to work with the others, who comprise the vast majority of all scientific and development workers. Management is to be criticized if a direct competition exists between some of their best workers and others much less able. It is usually a rather thoughtless application of the widespread principle that people work best when competing. If the problem can, instead, be divided so that quite different aspects are allocated to each group, the prospects are more favorable. It seems to me that statistically de- signed experiments are more likely to be useful near the very beginning of a project, (plans of Resolution I, II, III), and again near its end (plans of Resolu- tion IV, V, VI).

Some organizational situations appear unfavorable because an artificial dilemma has been posed: Shall we do it our own familiar way, or shall we do it this funny new way? As W. J. Youden has suggested, it is sometimes feasible to divide the program into two parts, one done each way.

Some statisticians would say that the only favorable time to enter a research project is at its beginning. My own experience does not confirm this. I have entered projects at all stages of their development. I do not see any connection between my stage of entry and my success or failure.

243

This content downloaded from 188.72.126.182 on Sat, 21 Jun 2014 00:14:39 AMAll use subject to JSTOR Terms and Conditions

I have given some sketchy indication of what I believe about good problems, about ready clients, and about favorable organizational conditions.

When all these conditions appear to be met, the consultant should be able to make up his mind whether, in the time available, he can make a major con- tribution to the work. My earnest advice is: If you do not feel that you can make a major contribution to the project, do not take the job. A major contribution is one that is recognizable as such by the project leader, with no cost-effectiveness study or other high-precision device required.

Coming now to the preparation, character, and educational background of a good consultant, I fear my recommendations may not please all of this audience. I put first a good scientific background. This should have been started in his undergraduate days; and it should include good training in elementary mathe- matics (calculus, analytic geometry, algebra, and standard matrix operations), as well as some physics, biology, and chemistry. Later reading in science, both in the many excellent lay-scientific journals and in the newer texts, will be con- tinuously useful to him.

I put second a man's general attitude and manner toward technical people. The more he enjoys cooperative enterprise, the more satisfaction he gets from being helpful, and the less his need to be dominant, the better consultant he will be. Brashness, arrogance, and superciliousness have lost us many more opportunities than client prejudices and limited statistical competence combined.

I put third a few years of experience in the study of real data and of real ex- periments, preferably (but not necessarily) under the supervision of a seasoned statistician.

I put academic statistical training fourth. You will remember that my experi- ence is limited to applications of standard multiple regression and design of

experiments. These might be thought to be rather well-understood and even rather elementary topics. But I do not find them handled academically with sufficient realism. I am unable to find a text, or combination of texts, that con- tains more than half of all that I think should be known about either of these

topics by a student with a master's degree in statistics. Since it appears (I have made no survey) that most Ph.D.'s in statistics are

expected to go into statistical research or into teaching what they have learned, I do not consider them the only candidates for consultancies in statistics. Al-

though its members are hard to locate, a much larger source of potential data

analysts and consultants is the group who do not hold advanced degrees in

statistics but who do have the other qualifications. To summarize, a man with a good general scientific background, with a positive

attitude toward being of service to others, and with a few years of experience studying real data under mature guidance, may well make a good consultant, even if he has no higher degree in statistics.

In closing, I have a few words to say about short courses in statistics. Such

courses may be useful. But their real and realizable objectives are not usually stated to management. These objectives are, I believe, some mix of the following related aims:

1. To find one man who will become the client's local statistician;

244 CUTHBERT DANIEL

This content downloaded from 188.72.126.182 on Sat, 21 Jun 2014 00:14:39 AMAll use subject to JSTOR Terms and Conditions

CONSULTING IN STATISTICS

2. To impress a group of R&D workers with the enthusiasm, even competence, of the lecturer;

3. To make work for the lecturer (or for the lecturer's company) as a con- sultant.

The objectives of such a course are usually stated in terms like these:

1. To acquaint R&D workers with the basic statistical concepts required to understand and interpret their data;

2. To show them how to plan, carry out, and analyze complex experimental programs;

3. etc.

I do not think that a three or five day course can take more than a very small sub-set of a group of 20 to 50, all the way from expected values to non-linear multiple regression and leave them with a usable residue. The reluctance of teachers of these courses to permit the mildest forms of objective evaluation of what has been learned supports this belief.

My own experience (perhaps fifty per cent relative successes, the rest failures) leads me to believe that the best subject-matter for a short course is an account of a successful campaign at the same laboratory or plant. Such a course should be partly presented by the clients who took part. My advice then is to start with a problem or project, and to get well along with that before you offer a course.

Short courses given for practicing statisticians by senior or consulting stat- isticians, are another thing. They can be very useful refreshers and up-daters. Their objectives can usually be written more concretely and modestly, and so have a much better chance of being attained.

To summarize once more: When ready clients in a favorable organizational situation have a good R or D problem, an able consultant can often make a major contribution. All these conditions should be assessed in approaching a new prob- lem. I should add, then, to my list of requirements for a well-prepared consultant, the ability to size up a situation quickly even when under stress. As Yogi Berra once said, "You can observe a lot by just watching".

245

This content downloaded from 188.72.126.182 on Sat, 21 Jun 2014 00:14:39 AMAll use subject to JSTOR Terms and Conditions