Dave Kerridge ( -2013), 我們的導師

Facebook 說，我"在2009年7月13日和 Dave Kerridge 成為朋友。"其實，我們都只在上頭簽個名，沒發聲。
2013年春，Dave Kerridge過世，我用英文寫篇Remembering Dave Kerridge；同年10月21，我用中文雜記交情：Dave Kerridge ( -2013), 我們的導師。

紀念David Kerridge

今天讀一則新聞:

On Oct. 31, Q2 Music celebrates new music's favorite holiday, Halloween, with Q2's first 24-hour scarathon of hair-raising microtones, densely clustered choruses and heart-pumping slasher film suites: http://bit.ly/16kuHGv

查一單字字根寫進”英文人行道”BLOG

其實這字根十幾年前英國的David Kerridge教授在email就教過我了.

今年他過世了. 我用英文寫篇感謝給其家人.

我們從未謀面.

-athon

Entry from World dictionary

suffix

• forming nouns denoting an action or activity which is carried on for a very long time or on a very large scale, typically to raise funds for charity:talkathon walkathon

Origin:

on the pattern of (mar)athon

2013.5.13

Thank you for preparing this piece of David's story for us. The Funeral service will be on Monday 20th May at Kings College Chapel, Aberdeen University. Anyone who is able to attend is welcome.

If we could have your story of how you knew David and the input he had on your life before then, we would be grateful.

Thank You

Deborah Armstrong

2013.5.13晨
從法國朋友Jean-Marie Gogue知道David Kerridge教授的惡耗 ( 5月9日過世   20日舉行追思禮拜)

Dear Hanching,

It is with great sadness that I have learnt the death of David Kerridge.

Best wishes

Jean-Marie

De : "JOYCE Orsini [Staff/Faculty [Business]]"

Objet : Sad News about David Kerridge

Date : 12 mai 2013 16:41:56 UTC+02:00

À : Jean-Marie Gogue

Dear Jean-Marie,

I don't know if you knew David Kerridge.  If so, you may wish to know that he passed away on Thursday.  I received a message from his daughter to that effect.

She said:  "We are collating stories about Dad for his funeral service on Monday 20th May. If you have any you would like to share please email them to me dalife@hotmail.co.uk"

JO

我簡單給他回信:

Dear J-M,

Thanks for this information.

It is really a sad news.

I wished to have a collection of his and her daughter's essays published.

同時我給美國的Jo寫信說     我要寫David過去17年對我們的解惑和幫助

Dear Jo,

I received Dave's sad news from J-M.

I'll write his story of  helping  me to understand Deming's and Shewhart's philosophy  and shared his wisdom with the readers in Taiwan.

Please kindly tell me the deadline of my story.

Deming Papers
www.fr-deming.org, 1 Nov 1980 [cached]

David Kerridge, former professor at the Aberdeen University, was a leader of the British Deming Association. He assisted at Deming four-day seminars for many years, and lectured with him in a series of two-day seminars.

Dave and Sarah Kerridge, "Aristotle's Mistake or the Curious Incident of the Dog in the Night-Time" Posing the right questions is more difficult than getting answers. Only the right question leads to the right answer. This short paper shows that by nature man is inclined to ask the wrong questions. A conscious effort is therefore required to look at things from a different viewpoint. 130 KB

Papers 2 &3 on Resistance to Change by David Kerridge

Paper 2

I want to suggest a theory about why there should be such resistance, and how it relates to our problems of spreading the Deming Philosophy.
I believe that the three approaches to statistical inference do not come just from differences about statistics. They correspond to three different views of what *science* is about. What follows is not an exact description of what philosophers say (or said) that scientists should do, but based on my own experience in using all three approaches, and observing what other scientists do.

1. Logical/Mathematical
   • Science is concerned with logical proof. It therefore requires an all or nothing view - a theory is true or false, and must be accepted or rejected.
2. Explanatory
   • Science is concerned with explanation - the reasons why things happen. Approximate models, like representing atoms by billiard balls, are useful, if they make the explanation easier to understand.
3. Predictive
   • Science is prediction - no more, no less. Prediction must be based on observation, and observation defined in terms of action.

These three views of science correspond to different stages of development. The logical/mathematical view was unquestioned throughout the middle ages, and reached its peak with Descartes. Scientific truths were deduced by strict logic, starting with self-evident axioms, as in Euclidean geometry.
From the time of Isaac Newton, science changed. I believe (I have not checked the originals) that Newton presented his ideas in the old format, as deductions from axioms. So successful was he that some later writers claimed that Newton's physics is true, not because of observed facts, but by pure logic. But for most people, what Newton did was to provide an explanation - the force of gravity.
Explanation need not be exact. As George Box has put it: "All models are wrong. Some are useful."
At the beginning of the 20th century, both forms of science fell apart. There were two blows to previous thinking. First of all, many "self-evident" ideas turned out to be false. An example is Einstein's demonstration that time is relative. Secondly, the idea of explanation itself was called into question.
Quantum Theory, in particular, provides no explanation we can understand. But it predicts strange and unbelievable outcomes, and predicts them with amazing accuracy.
Most people are unaware that science has changed. Only those trained in theoretical physics (like Shewhart and Deming) have adopted the new philosophy of science. Others are stuck in the thinking of earlier centuries. And because knowledge is now so specialised and compartmentalised, few scientists are aware that different ideas are taken for granted in fields other than their own. We are dealing, in most cases, with unconscious assumptions, rather than conscious beliefs. That makes them far harder to deal with. It seems that many people cannot face a challenge to what they think is "obvious" - though the System of Profound Knowledge is one challenge after another.
My theory about the three approaches to statistics is as follows.

1. Neyman and Pearson saw science in the logical/deductive mode, which is still common among mathematicians.
2. R A Fisher had extensive experience of biological science. He became, in fact the head of the department of Genetics at Cambridge. Like most scientists, he saw science as explanation.
3. Walter A Shewhart and W Edwards Deming saw science in terms of the new physics of prediction and action.

I started with statistics because the historical record is so striking. But the other examples are also well documented. Semmelweiss demonstrated that hygiene saved lives. But nothing was done until Pasteur explained the reason for it.
I apologise for what may seem to be lengthy theoretical rambling. But the strange thing about the Deming philosophy is that the most abstract ideas turn out to have direct practical applications. It is not surprising that science based on prediction and action is exactly what we need for management.
In my next instalment I hope to show that this helps us understand some of the difficulties we face.
-----------------------------------------------------------------------------------------------

Paper 3

In the previous post I mentioned conflicting models of science, based on either:

1. Logical deduction
2. Models and explanation
3. Prediction

The idea of different models of science may seem remote from practical application. But as I expect we have all found for ourselves, in the Deming Philosophy nothing is "too theoretical" to affect our actions.
Walter A Shewhart certainly saw this. In his 1931 book, on "The Economic Control of Quality of Manufactured Product" he wrote:
"Progress in modifying our concept of control has been and will be comparatively slow. In the first place, it requires the application of certain modern physical concepts......"
He does not say which concepts. But we can reasonably relate this to the "Theory of Knowledge" dimension of the System of Profound Knowledge.
It seems to me - and this is purely a personal reflection which others may dispute - that the "Theory of Knowledge" aspect is the one that makes least impact. It may easily sound as if it contains nothing new. After all, most scientists, if asked, would say something similar. They would probably quote Karl Popper, who popularised this view of science, rather than C I Lewis or A N Whitehead, but the message is the same.
The difference is - again a personal opinion - that most people, whether scientists or laymen, pay lip-service to Popper, but continue to think in earlier modes. Most people see the whole point of science as explanation.
I have just been watching a television series that attempts to explain the ideas of "String Theory" for the layman. It showed one group of scientists arguing that "strings" may provide the "theory of everything" that unites Quantum Theory and Relativity, which are at present in conflict. Other scientists say "Strings are not a scientific theory" because they make no testable predictions. I can almost hear Shewhart laughing.
But "see" is the key word here. What we see determines how we act. WED describes the System of Profound Knowledge as a "lens". In other words, it enables us to bring some things into focus, and to see what we could not otherwise see.
What a pure scientist sees may change the whole world in the long run. What a manager sees changes everything now. To quote WED's own words:
"My job is not to tell managers what to do. It is to help them to see things that they could not otherwise be expected to see."
We have all seen how managers react to the Red Bead Experiment. The idea that it is wrong to look for an explanation of the red beads produced by a worker is profoundly shocking. Explanation is the lens through which they see the world. It is very hard to switch to thinking in terms of prediction.
This creates resistance to what Deming and Shewhart say about SPC, systems, and even psychology. The problem is all the greater because it is unconscious.






+++++

This is an expanded version of an article that Balestracci wrote for Quality Digest in December 2007.
--Editor

Idiscovered a wonderful unpublished paper by David and Sarah Kerridge several years ago (Click here to get a pdf). Its influence on my thinking has been nothing short of profound. As statistical methods get more and more embedded in everyday organizational quality improvements, I feel that now is the time to get us "back to basics"—but a set of basics that is woefully misunderstood, if taught at all. Professor Kerridge is an academic at the University of Aberdeen in Scotland, and I consider him one of the leading Deming thinkers in the world today.

Deming distinguished between two types of statistical study, which he called "enumerative" and "analytic." The key connection for quality improvement is about the way that statistics relates to reality and lays the foundation for a theory of using statistics.

Because everyday processes are usually not static "populations," the question becomes, "What other knowledge, beyond probability theory, is needed to form a basis for action in the real world?" The perspective from which virtually all college courses are taught—population based—invalidates many of its techniques in a work environment, as opposed to a strictly research environment.

To translate to medicine, there are three kinds of statistics:

• Descriptive . What can I say about this specific patient?
• Enumerative. What can I say about this specific group of patients?
• Analytic. What can I say about the process that produced this specific group of patients and its results?

Let's suppose there is a claim that, as a result of a new infection-control policy, acquired-MRSA (methicillin-resistant Staphylococcus aureus, a strain of staph that is resistant to the broad-spectrum antibiotics commonly used to treat infections) in a particular hospital has been reduced by 27 percent—a result that would be extremely desirable if that kind of reduction could be produced in other hospitals, or in public health communities, by using the same methods. However, there are a great many questions to ask before we can act, even if the action is to design an experiment to find out more.

Counting the number of infections in different years is an enumerative problem (defining "acquired infection" and counting them for this specific hospital). Interpreting the change is an analytic problem.

Could the 27-percent reduction be due to chance? If we imagine a set of constant conditions, which would lead, on average, to 100 infections, we can, on the simplest mathematical model (Poisson counts), expect the number we actually see to be anything between 70 and 130. If there were 130 infections one year, and 70 infections the next year, people would think that there had been a great improvement—but this could be just chance. This is the least of our problems.

Some of the infections may be related, as in a temporary outbreak or pandemic. If so, the model is wrong, because it assumes that infections are independent; or the methods of counting might have changed from one year to the next (Are you counting all suspicious infections, or only confirmed cases?). Without knowing about such things we cannot predict from these figures what will happen next year. So if we want to draw the conclusion that the 27-percent reduction is a "real" one, that is, one which will continue in the future, we must use knowledge about the problem that is not given by those figures alone.

Even less can we predict accurately what would happen in a different hospital, or a different country. The causes of infection, or the effect of a change in infection control methods, may be completely different.

So this is the context of the distinction between enumerative and analytic uses of statistics. Some things can be determined by calculation alone, others require the use of judgment or knowledge of the subject, others are almost unknowable. Luckily, your actions to get more information inherently improve the situation, because when you understand the sources of uncertainty, you understand how to reduce it.

Most mathematical statisticians state statistical problems in terms of repeated sampling from the same population. This leads to a very simple mathematical theory, but does not relate to the real needs of the statistical user. You cannot take repeated samples from the exact same population, except in rare cases. It's a different kind of problem—sampling from an imaginary population.