The End of OR at UC

Blogs are by their nature, personal. Today’s blog is even more personal as I tell of my life with Operations Research and the demise of OR at UC.

Operations Research is a useful, interesting and challenging subject.

I love Operations Research. It was love at first sight, and though I now teach statistics, it is with an attitude strongly shaped by Operations Research.

At school I loved maths. And I was good at it. I captained a team that won the city “Cantamath” competition two years running in the early 1970s. In high school I had a great maths teacher who let me be an assistant to the others in class when I had finished my work. That cemented my desire to be a high-school maths teacher.

I went to university,  intending to become a maths teacher, but unsure about my backup subjects. I took an Economics course, which had six weeks of Operations Research in it. I was sold. This was the subject I had been born for – practical use of numbers to make things better. So I changed my major to Operations Research, and took enough mathematics to still be able to teach at all levels. The lack of numbers and practical application in maths courses above the introductory level left me cold. I added in computer science – and loved programming. My introduction to Statistics was a total mystery, dominated by probability taught through gambling examples, but I managed to get an A anyway.

Fast-forward a couple of decades. I am now at the end of my official operations research career. For twenty years I have been teaching introductory Operations Research and various levels of applied statistics. I completed a PhD thesis on the allocation of resources for the education of students with vision impairment, which used OR methodology and hierarchical linear modeling, though I also dabbled with Data Envelopment Analysis. I have been innovative in my teaching of OR and statistics and won a university teaching award. My videos to teach Excel, Statistics and linear programming have been well-received internationally. I have been fortunate to have worked with many wonderful academics and thousands of mostly wonderful students. (And always wonderful ancillary staff, but don’t get me started on management!)

What I love about Operations Research is the problem-solving practical nature of the work we do. Through student projects I have helped schedule hospital beds and scientific visits to Antarctica. We have helped local government, chicken factories and large trucking firms. We have made things better. When I go to Operations Research conferences I love to hear stories of how OR is helping in less developed countries, and in disaster relief and in so many ways. OR does good. OR makes things better. OR is lots of fun.

So why is my official time with OR over? On Wednesday the Council of the university at which I have been employed voted to close down the Operations Research programme. The university wants to “concentrate” and OR didn’t make the grade, despite two academics taking voluntary redundancy, and a concerted effort to streamline the programme so that it is financially viable. It is the end of an era. In the ultimate irony, the following day I was helping with community outreach and met a student trying to decide what subjects to take. She wanted something that used maths, but wasn’t engineering, and had a people component to it, and possibly was related to business. I told her I knew the perfect subject for her, but that she would not be able to take it our university. I tried to sell her on Operations Management, but I hope I wasn’t too convincing.

So now I will be leaving the university and focussing on bringing statistics to the masses. Statistics is my third love, after mathematics and operations research. I feel a calling to use the operations research way of thinking to help people to understand and enjoy statistics. And thus was born this blog and my future ventures. Statistics is so often taught in a way that confuses people. It is taught by mathematicians who do not understand that most of their students are not. My desire is to help both the teachers and the students so that people understand statistics better. I have not abandoned OR, and we will also be producing materials to help in teaching that. But it might have to step back-stage for a while.

At this sad time I have been enormously buoyed up by comments on my Youtube channel, CreativeHeuristics. Here is a recent one about “Understanding the P-value”

“Thank you for making statistics easy to follow in an entertaining way! You definitely help students like me who have difficulty following the concepts…your method of teaching works perfect for me b/c it helps me get it! I appreciate how your videos simplify the explanations so that its easy to follow. Thank you!”

Comments like this warm the cockles of my heart. It makes it SO worthwhile.

This time is the start of an adventure. I have loved most of my time as an academic, but never really did enough research as I was seduced by the joy of teaching. I am fortunate to have now the opportunity to start a new career after twenty years, and can see the possibilities as well as the hazards. I am enormously fortunate to have a supportive husband and an enthusiastic business partner.

Watch this space!

(And if you want to help us, please buy our apps –  AtMyPace:Statistics and Rogo.)


Seductive Causation

Causation is a seductive notion. We want to make meaning out of our world.

I love playing “the beeping nose” with little children. I press their nose and it beeps. I press my nose and it whirrs. It fascinates them. They have discovered cause and effect. They can make cool sounds by pressing noses. You can keep them amused for quite some time.

Cause and effect implies control. If we know what causes things we are better able to control them. Scientific endeavor is largely a search for causes.

History is littered with examples of misplaced cause and effect theories. Many of them apply to medicine, and still do. Gerd Gigerenzer cites the example of Rudi Giuliani claiming victory over socialized medicine. Giuliani points out that that life expectancy for men diagnosed with prostate cancer is longer in the US than in the UK. Gigerenzer points out that Giuliani omits to mention that they all live about the same length of time from contracting the disease but that because of screening, American men are aware of their illness for longer. And many would not see that awareness as a plus, especially combined with the high rate of false positives and consequent nasty side-effects.

Association can imply several different explanations

In the early days of autism research the blame was often placed on “refrigerator mothers” – mothers who did not show warmth to their babies. This was a result of doctors’ observations of the mothers of children with autism. This has since been discredited as a cause. It is suggested that the mothers were acting that way in response to their baby. It takes two to bond.

It is difficult to prove causation. In any identified statistical correlation or association there are multiple explanations. Effects A and B are found to be related. It could be that A causes B. But maybe B causes A. Or a third possibility is that C causes both A and B. Passage of time may be the universal factor.

Granny cures the common cold

This reminds me of a Beverly Hillbillies episode where Granny has a cure for the common cold. One spoonful is enough and you are sure to be cured! Miss Hathaway gets all excited about this entrepreneurial opportunity until Granny explains that it takes about ten days to get better. But her patients always do! I think a control group might have been helpful in this instance.

The prevalence of misplaced causation is one of the most important concepts that a teacher of statistics can teach. We need to make sure the citizens of the world take a critical approach to claims of causation.

So how do we teach this?

I don’t have the answer to this one, other than that I know we should try. Stories and more stories, I suspect. Have them identify types of data. Placing labels on things helps. Make sure they can identify observational, epidemiological and experimental data. Get them to think up alternative explanations, and identify misplaced claims of causation. I find True/False questions remarkably useful in challenging students’ thinking.

Unfortunately causation is probably an example where “school learning” and real learning may part company. The students will give the correct answer using their own dysfunctional rules, such as “If the statement includes the word “causes”, it must be false”. But then again, maybe that’s not such a bad rule!

If students laugh at this I think they "get" causation.