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I’m kidding about poets. But lots of people need to understand the three basic areas of statistics, **Chance, Data and Evidence**.

Recently Tony Greenfield, an esteemed applied statistician, (with his roots in Operations Research) posted the following request on a statistics email list:

“I went this week to the exhibition and conference in the NEC run by The Engineer magazine. There were CEOs of engineering companies of all sizes, from small to massive. I asked a loaded question: “Why should every engineer be a competent applied statistician?” Only one, from more than 100 engineers, answered: “We need to analyse any data that comes along.” They all seemed bewildered when I asked if they knew about, or even used, SPC and DoE. I shall welcome one paragraph responses to my question. I could talk all day about it but it would be good to have a succinct and powerful few words to use at such a conference.”

For now I will focus on civil engineers, as they are often what people think of as engineers. I’m not sure about the “succinct and powerful” nature of the words to follow, but here goes…

The subject of statistics can be summarised as three areas – **chance, data and evidence** (CDE!)

**Chance** includes the rules and perceptions of probability, and emphasises the uncertainty in our world. I suspect engineers are more at home in a deterministic world, but determinism is just a model of reality. The strength of a bar of steel is not exact, but will be modelled with a probability distribution. An understanding of probability is necessary before using terms such as “one hundred year flood”. Expected values are used for making decisions on improving roads and intersections. The capacity of stadiums and malls, and the provision of toilets and exits all require modelling that relies on probability distributions. It is also necessary to have some understanding of our human fallibility in estimating and communicating probability. Statistical process control accounts for acceptable levels of variation, and indicates when they have been exceeded.

The **Data** aspect of the study of statistics embraces the collection, summary and communication of data. In order to make decisions, data must be collected. Correct summary measures must be used, often the median, rather than the more popular mean. Summary measures should preferably be expressed as confidence intervals, thus communicating the level of precision inherent in the data. Appropriate graphs are needed, which seldom includes pictograms or pie charts.

**Evidence **refers to the inferential aspects of statistical analysis. The theories of probability are used to evaluate whether a certain set of data provides sufficient evidence to draw conclusions. An engineer needs to understand the use of hypothesis testing and the p-value in order to make informed decisions regarding data. Any professional in any field should be using evidence-based practice, and journal articles providing evidence will almost always refer to the p-value. They should also be wary of claims of causation, and understand the difference between strength of effect and strength of evidence. Our video provides a gentle introduction to these concepts.

Design of Experiments also incorporates the Chance, Data and Evidence aspects of the discipline of statistics. By randomising the units in an experiment we can control for other extraneous elements that might affect the outcome in an observational study. Engineers should be at home with these concepts.

So, Tony, how was that? Not exactly succinct, and four paragraphs rather than one. I think the Chance, Data, Evidence framework helps provide structure to the explanation.

I borrow the term from Peter Bell of Richard Ivey School of Business, who teaches operations research to MBA students, and wrote a paper, Operations Research For Everyone (including poets). If it is difficult to get the world to recognise the importance of statistics, how much harder is it to convince them that Operations Research is vital to their well-being!

Bell uses the term, “poet” to refer to students who are not naturally at home with mathematics. In conversation Bell explained how many of his poets, who were planning to work in the area of human resource management found their summer internships were spent elbow-deep in data, in front of a spreadsheet, and were grateful for the skills they had resisted gaining.

An understanding of **chance, data and evidence** is useful/essential for “efficient citizenship”, to paraphrase the often paraphrased H. G. Wells. I have already written on the necessity for journalists to have an understanding of statistics. The innovative New Zealand curriculum recognises the importance of an understanding of statistics for all. There are numerous courses dedicated to making sure that medical practitioners have a good understanding.

So really, there are few professions or trades that would not benefit from a grounding in **Chance, Data and Evidence**. And Operations Research too, but for now that may be a bridge too far.

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## 3 Comments

As an engineer (first) *and* a statistician (latterly), I agree. Engineers should be competant applied statisticians. Mind you, engineers need to be applied mathematicians too, sometimes exhibiting some of the skills one might expect of an operations research specialist as well, sometimes with management skills, with some physics and sometimes chemistry thrown in there too (depending on the sub-field you happen to inhabit). My “double life” as an engineer and an applied statistician has taught me that both very often inhabit that part of the problem space that has to be “solved”, rather than worked on. Colleagues (trained in classic sciences) see a problem statement as a means to continue the evolving nature of a problem they are interested in. Engineers (and applied statisticians, and probably technologists in general) seem to see a problem as having a beginning, a middle, and an end. I think engineers (and statisticians too) need to learn more about this mirky world of general problem solving, and that there really isn’t just one way to approach its solution.

Nic – there are some neat phrases in your reply to Tony Greenfield, but it still seems to me to be too broad. Or to put it another way, it has a statistical logic (chance, data, evidence) with engineering examples tacked on. We need to turn this on its head and start from the heart of the ‘other’ discipline. A key success criterion for an engineered system is minimal variation (it does what it is supposed to do every time) – statistical methods allow engineers to understand, measure and reduce variation. Statistics has lots of specific applications in engineering (experimentation and modelling, process control, problem-solving) but the ability to handle variation is the starting point.

Very nice. Much better than mine, which is as you say!