# The normal distribution – three tricky bits

There are several tricky things about teaching and understanding the normal distribution, and in this post I’m going to talk about three of them. They are the idea of a model, the limitations of the normal distribution, and the idea of the probability being the area under the graph.

# It’s a model!

When people hear the term distribution, they tend to think of the normal distribution. It is an appealing idea, and remarkably versatile. The normal distribution is an appropriate model for the outcome of many natural, manufacturing and human endeavours. However, it is only a model, not a rule. But sometimes the way we talk about things as “being normally distributed” can encourage incorrect thinking.

This problem can be seen in exam questions about the application of the normal distribution. They imply that the normal distribution controls the universe.

Here is are examples of question starters taken from a textbook:

1. “The time it takes Steve to walk to school follows a normal distribution with mean 30 minutes…”.
2. Or “The time to failure for a new component is normally distributed with a mean of…”

This terminology is too prescriptive. There is no rule that says that Steve has to time his walks to school to fit a certain distribution. Nor does a machine create components that purposefully follow a normal distribution with regard to failure time. I remember, as a student being intrigued by this idea, not really understanding the concept of a model.

When we are teaching, and at other times, it is preferable to say that things are appropriately modelled by a normal distribution. This reminds students that the normal distribution is a model. The above examples could be rewritten as

1. “The time it takes Steve to walk to school is appropriately modelled using a normal distribution with mean 30 minutes…”.
2. And  “The time to failure for a new component is found to have a distribution well modelled by the normal, with a mean of…”

They may seem a little clumsy, but send the important message that the normal distribution is the approximation of a random process, not the other way around.

# Not everything is normal

It is also important that students do not get the idea that all distributions, or even all continuous distributions are normal. The uniform distribution and negative exponential distributions are both useful in different circumstances, and look nothing like the normal distribution. And distributions of real entities can often have many zero values, that make a distribution far from normal-looking.

The normal distribution is great for things that measure mostly around a central value, and there are increasingly fewer things as you get further from the mean in both directions. I suspect most people can understand that in many areas of life you get lots of “average” people or things, and some really good and some really bad. (Except at Lake Wobegon “where all the women are strong, all the men are good looking, and all the children are above average.”)

However the normal distribution is not useful for modelling distributions that are heavily skewed. For instance, house prices tend to have a very long tail to the right, as there are some outrageously expensive houses, even several times the value of the median. At the same time there is a clear lower bound at zero, or somewhere above it.

Inter-arrival times are not well modelled by the normal distribution, but are well modelled by a negative exponential distribution. If we want to model how long it is likely to be before the next customer arrives, we would not expect there to be as many long times as there are short times, but fewer and fewer arrivals will occur with longer gaps.

Daily rainfall is not well modelled by the normal distribution as there will be many days of zero rainfall. Amount claimed in medical insurance or any kind of insurance are not going to be well modelled by the normal distribution as there are zero claims, and also the effect of excesses. Guest stay lengths at a hotel would not be well modelled by the normal distribution. Most guests will stay one or two days, and the longer the time, the fewer people would stay that long.

# Area under the graph – idea of sand

The idea of the area under the graph being the probability of an outcome’s happening in that range is conceptually challenging. I was recently introduced to the sand metaphor by Holly-Lynne  and Todd Lee. If you think about each outcome as being a grain of sand (or a pixel in a picture) then you think about how likely it is to occur, by the size of the area that encloses it. I found the metaphor very appealing, and you can read the whole paper here:

Visual representations of empirical probability distributions when using the granular density metaphor

There are other aspects of the normal distribution that can be challenging. Here is our latest video to help you to teach and learn and understand the normal distribution.

# Introducing Probability

I have a guilty secret. I really love probability problems. I am so happy to be making videos about probability just now, and conditional probability and distributions and all that fun stuff. I am a little disappointed that we won’t be doing decision trees with Bayesian review, calculating EVPI. That is such fun, but I gave up teaching that some years ago.

The reason probability is fun is because it is really mathematics, and puzzles and logic. I love permutations and combinations too – there is something cool about working out how many ways something can happen.

So why should I feel guilty? Well, in all honesty I have to admit that there is very little need for most of that in a course about statistics at high-school or entry level university. When I taught statistical methods for management, we did some probability, but only from an applied viewpoint, and we never touched intersection and union signs or anything like that. We applied some distributions, but without much theoretical underpinning.

The GAISE (Guidelines for Assessment and Instruction in Statistics Education) Report says, “Teachers and students must understand that statistics and probability are not the same. Statistics uses probability, much as physics uses calculus.”

The question is, why do we teach probability – apart from the fact that it’s fun and makes a nice change from writing reports on time series and bivariate analysis, inference and experiments. The GAISE report also says, “Probability is an important part of any mathematical education. It is a part of mathematics that enriches the subject as a whole by its interactions with other uses of mathematics. Probability is an essential tool in applied mathematics and mathematical modeling. It is also an essential tool in statistics.”

The concept of probability is as important as it is misunderstood. It is vital to have an understanding of the nature of chance and variation in life, in order to be a well-informed, (or “efficient”) citizen. One area in which this is extremely important is in understanding risk and relative risk. When a person is told that their chances of dying of some rare disease have just doubled, it is important that they know that it may be because they have gone from one chance in a million to two chances in a million. Sure it has doubled, but it still is pretty trivial. An understanding of probability is also important in terms of gambling and resistance to the allures of games of chance. And more socially acceptable gambling, such as stockmarket trading, also requires an understanding of chance and variation.

The concept of probability is important, and a few rules of probability may help with understanding, but I suspect the mathematicians get carried away and create problems that are unlikely (probability close to zero) to ever occur in reality. Anything requiring a three-way Venn Diagram has moved from applied problem to logic puzzle.This is in stark contrast to the very applied data-driven approach used in teaching statistics in New Zealand.

## Teaching Probability

The traditional approach to teaching probability is to start with the coin and the dice and the balls in the urns. As well as being mind-bogglingly boring and pointless, this also projects an artificial certainty about the probabilities, which is confusing when we start discussing models. If you look at the Khan Academy videos (but don’t) you will find trivial examples about coloured balls or sweets or strangely complex problems involving hitting a circular target. The traditional approach is also to teach probability as truth. “The probability of getting a boy is one-half”. What does that even mean?

I am currently reading the new Springer volume, Probabilistic Thinking, and intend to write a review and post it on this blog, if I can get through enough before my review copy expires. It is inspiring and surprisingly gripping (but I don’t think that is enough of a review to earn me a hard copy to keep.). There are many great ideas for teaching in it, that I hope to pass on in due time.

The New Zealand approach to teaching probability comes from a modelling perspective, right from the start. At level 1, the first two years of schooling, children are exploring chance situations, playing games with a chance element and describing possible outcomes. By years 5 and 6 they are assigning numeric values to the likelihood of an occurrence. They (in the curriculum) are being introduced to model estimates and experimental estimates of probability. Bearing in mind how difficult high school maths teachers are finding the new approach, I don’t have a lot of confidence that the primary teachers are equipped yet to make the philosophical changes, let alone enact them in the classroom.

# Deterministic and Probabilistic models and thinking

The way we understand and make sense of variation in the world affects decisions we make.

Part of understanding variation is understanding the difference between deterministic and probabilistic (stochastic) models. The NZ curriculum specifies the following learning outcome: “Selects and uses appropriate methods to investigate probability situations including experiments, simulations, and theoretical probability, distinguishing between deterministic and probabilistic models.” This is at level 8 of the curriculum, the highest level of secondary schooling. Deterministic and probabilistic models are not familiar to all teachers of mathematics and statistics, so I’m writing about it today.

## Model

The term, model, is itself challenging. There are many ways to use the word, two of which are particularly relevant for this discussion. The first meaning is “mathematical model, as a decision-making tool”. This is the one I am familiar with from years of teaching Operations Research. The second way is “way of thinking or representing an idea”. Or something like that. It seems to come from psychology.

When teaching mathematical models in entry level operations research/management science we would spend some time clarifying what we mean by a model. I have written about this in the post, “All models are wrong.”

In a simple, concrete incarnation, a model is a representation of another object. A simple example is that of a model car or a Lego model of a house. There are aspects of the model that are the same as the original, such as the shape and ability to move or not. But many aspects of the real-life object are missing in the model. The car does not have an internal combustion engine, and the house has no soft-furnishings. (And very bumpy floors). There is little purpose for either of these models, except entertainment and the joy of creation or ownership. (You might be interested in the following video of the Lego Parisian restaurant, which I am coveting. Funny way to say Parisian!)

Many models perform useful functions. My husband works as a land-surveyor, and his work involves making models on paper or in the computer, of phenomenon on the land, and making sure that specified marks on the model correspond to the marks placed in the ground. The purpose of the model relates to ownership and making sure the sewers run in the right direction. (As a result of several years of earthquakes in Christchurch, his models are less deterministic than they used to be, and unfortunately many of our sewers ended up running the wrong way.)

Our world is full of models:

• a map is a model of a location, which can help us get from place to place.
• sheet music is a written model of the sound which can make a song
• a bus timetable is a model of where buses should appear
• a company’s financial reports are a model of one aspect of the company

## Deterministic models

A deterministic model assumes certainty in all aspects. Examples of deterministic models are timetables, pricing structures, a linear programming model, the economic order quantity model, maps, accounting.

## Probabilistic or stochastic models

Most models really should be stochastic or probabilistic rather than deterministic, but this is often too complicated to implement. Representing uncertainty is fraught. Some more common stochastic models are queueing models, markov chains, and most simulations.

For example when planning a school formal, there are some elements of the model that are deterministic and some that are probabilistic. The cost to hire the venue is deterministic, but the number of students who will come is probabilistic. A GPS unit uses a deterministic model to decide on the most suitable route and gives a predicted arrival time. However we know that the actual arrival time is contingent upon all sorts of aspects including road, driver, traffic and weather conditions.

## Model as a way of thinking about something

The term “model” is also used to describe the way that people make sense out of their world. Some people have a more deterministic world model than others, contributed to by age, culture, religion, life experience and education. People ascribe meaning to anything from star patterns, tea leaves and moon phases to ease in finding a parking spot and not being in a certain place when a coconut falls. This is a way of turning a probabilistic world into a more deterministic and more meaningful world. Some people are happy with a probabilistic world, where things really do have a high degree of randomness. But often we are less happy when the randomness goes against us. (I find it interesting that farmers hit with bad fortune such as a snowfall or drought are happy to ask for government help, yet when there is a bumper crop, I don’t see them offering to give back some of their windfall voluntarily.)

Let us say the All Blacks win a rugby game against Australia. There are several ways we can draw meaning from this. If we are of a deterministic frame of mind, we might say that the All Blacks won because they are the best rugby team in the world.  We have assigned cause and effect to the outcome. Or we could take a more probabilistic view of it, deciding that the probability that they would win was about 70%, and that on the day they were fortunate.  Or, if we were Australian, we might say that the Australian team was far better and it was just a 1 in 100 chance that the All Blacks would win.

I developed the following scenarios for discussion in a classroom. The students can put them in order or categories according to their own criteria. After discussing their results, we could then talk about a deterministic and a probabilistic meaning for each of the scenarios.

1. The All Blacks won the Rugby World Cup.
2. Eri did better on a test after getting tuition.
3. Holly was diagnosed with cancer, had a religious experience and the cancer was gone.
4. A pet was given a homeopathic remedy and got better.
5. Bill won \$20 million in Lotto.
6. You got five out of five right in a true/false quiz.

The regular mathematics teacher is now a long way from his or her comfort zone. The numbers have gone, along with the red tick, and there are no correct answers. This is an important aspect of understanding probability – that many things are the result of randomness. But with this idea we are pulling mathematics teachers into unfamiliar territory. Social studies, science and English teachers have had to deal with the murky area of feelings, values and ethics forever.  In terms of preparing students for a random world, I think it is territory worth spending some time in. And it might just help them find mathematics/statistics relevant!

# Twenty-first century Junior Woodchuck Guidebook

I grew up reading Donald Duck comics. I love the Junior Woodchucks, and their Junior Woodchuck Guidebook. The Guidebook is a small paperback book, containing information on every conceivable subject, including geography, mythology, history, literature and the Rubaiyat of Omar Khayyam.  In our family, when we want to know something or check some piece of information, we talk about consulting the Junior Woodchuck Guidebook. (Imagine my joy when I discovered that a woodchuck is another name for a groundhog, the star of my favourite movie!) What we are referring to is the internet, the source of all possible information! Thanks to search engines, there is very little we cannot find out on the internet. And very big thanks to Wikipedia, to which I make an annual financial contribution, as should all who use it and can afford to.

You can learn just about anything on the internet. Problem is, how do you know what is good? And how do you help students find good stuff? And how do you use the internet wisely? And how can it help us as learners and teachers of statistics and operations research? These questions will take more than my usual 1000 words, so I will break it up a bit. This post is about the ways the internet can help in teaching and learning. In a later post I will talk about evaluating resources, and in particular multimedia resources.

## Context

Both the disciplines in which I am interested, statistics and operations research, apply mathematical and analytic methods to real-world problems. In statistics we are generally trying to find things out, and in operations research we are trying to make them better. Either way, the context is important. The internet enables students to find background knowledge regarding the context of the data or problem they are dealing with. It also enables instructors to write assessments and exercises that have a degree of veracity to them even if the actual raw data proves elusive. How I wish people would publish standard deviations as well as means when reporting results!

## Data

Which brings us to the second use for on-line resources. Real problems with real data are much more meaningful for students, and totally possible now that we don’t need to calculate anything by hand. Sadly, it is more difficult than first appears to find good quality raw data to analyse, but there is some available. You can see some sources in a previous post and the helpful comments.

## Explanations

If you are struggling to understand a concept, or to know how to teach or explain it, do a web search. I have found some great explanations, and diagrams especially, that have helped me. Or I have discovered a dearth of good diagrams, which has prompted me to make my own.

## Video

Videos can help with background knowledge, with explanations, and with inspiring students with the worth of the discipline. The problem with videos is that it takes a long time to find good ones and weed out the others. One suggestion is to enlist the help of your students. They can each watch two or three videos and decide which are the most helpful. The teacher then watches the most popular ones to check for pedagogical value. It is great when you find a site that you can trust, but even then you can’t guarantee the approach will be compatible with your own.

## Social support

I particularly love Twitter, from which I get connection with other teachers and learners, and ideas and links to blogs. I belong to a Facebook group for teachers of statistics in New Zealand, and another Facebook group called “I love Operations Research”. These wax and wane in activity, and can be very helpful at times. Students and teachers can gain a lot from social networking.

## Software

There is good open-source software available, and 30-day trial versions for other software. Many schools in New Zealand use the R-based iNZight collection of programmes, which provide purpose-built means for timeseries analysis, bootstrapping and line fitting.

The other day I lost the volume control off my toolbar. (Windows Vista, I’m embarrassed to admit). So I put in the search box “Lost my volume control” and was directed to a YouTube video that took me step-by-step through the convoluted process of reinstating my volume control! I was so grateful I made a donation. Just about any computer related question can be answered through a search.

## Interactive demonstrations

I love these. There are two sites I have found great:

The National Library of Virtual Manipulatives, based in Utah.

A problem with some of these is the use of Flash, which does not play on all devices. And again – how do we decide if they are any good or not?

## On-line textbooks

Why would you buy a textbook when you can get one on-line. I routinely directed my second-year statistical methods for business students to “Concepts and Applications of Inferential Statistics”. I’ve found it just the right level. Another source is Stattrek. I particularly like their short explanations of the different probability distributions.

## Practice quizzes

There aren’t too many practice quizzes  around for free. Obviously, as a provider of statistical learning materials, I believe quizzes and exercises have merit for practice with immediate and focussed feedback. However, it can be very time-consuming to evaluate practice quizzes, and some just aren’t very good. On the other hand, some may argue that any time students spend learning is better than none.

## Live help

There are some places that provide live, or slightly delayed help for students. I got hooked into a very fun site where you earned points by helping students. Sadly I can’t find it now, but as I was looking I found vast numbers of on-line help sites, often associated with public libraries. And there are commercial sites that provide some free help as an intro to their services. In New Zealand there is the StudyIt service, which helps students preparing for assessments in the senior high school years. At StatsLC we provide on-line help as part of our resources, and will be looking to develop this further. From time to time I get questions as a result of my YouTube videos, and enjoy answering them ,unless I am obviously doing someone’s homework! I also discovered “ShowMe” which looks like a great little iPad app, that I can use to help people more.

This has just been a quick guide to how useful the internet can be in teaching and learning. Next week I will address issues of quality and equity.

# Khan academy probability videos and exercises aren’t good either

Dear Mr Khan

You have created an amazing resource that thousands of people all over the world get a lot of help from. Well done. Some of your materials are not very good, though, so I am writing this open letter in the hope that it might make some difference. Like many others, I believe that something as popular as Khan Academy will benefit from constructive criticism.

I fear that the reason that so many people like your mathematics videos so much is not because the videos are good, but because their experience in the classroom is so bad, and the curriculum is poorly thought out and encourages mechanistic thinking. This opinion is borne out by comments I have read from parents and other bloggers. The parents love you because you help their children pass tests.  (And these tests are clearly testing the type of material you are helping them to pass!) The bloggers are not so happy, because you perpetuate a type of mathematical instruction that should have disappeared by now. I can’t even imagine what the history teachers say about your content-driven delivery, but I will stick to what I know. (You can read one critique here)

Just over a year ago I wrote a balanced review of some of the Khan Academy videos about statistics. I know that statistics is difficult to explain – in fact one of the hardest subjects to teach. You can read my review here. I’ve also reviewed a selection of videos about confidence intervals, one of which was from Khan Academy. You can read the review here.

Consequently I am aware that blogging about the Khan Academy in anything other than glowing terms is an invitation for vitriol from your followers.

However, I thought it was about time I looked at the exercises that are available on KA, wondering if I should recommend them to high school teachers for their students to use for review. I decided to focus on one section, introduction to probability. I put myself in the place of a person who was struggling to understand probability at school.

## Here is the verdict.

First of all the site is very nice. It shows that it has a good sized budget to use on graphics and site mechanics. It is friendly to get into. I was a bit confused that the first section in the Probability and Statistics Section is called “Independent and dependent events”. It was the first section though. The first section of this first section is called Basic Probability, so I felt I was in the right place. But then under the heading, Basic probability, it says, “Can I pick a red frog out of a bag that only contains marbles?” Now I have no trouble with humour per se, and some people find my videos pretty funny. But I am very careful to avoid confusing people with the humour. For an anxious student who is looking for help, that is a bit confusing.

I was excited to see that this section had five videos, and two sets of exercises. I was pleased about that, as I’ve wanted to try out some exercises for some time, particularly after reading the review from Fawn Nguyen on her experience with exercises on Khan Academy. (I suggest you read this – it’s pretty funny.)

So I watched the first video about probability and it was like any other KA video I’ve viewed, with primitive graphics and a stumbling repetitive narration. It was correct enough, but did not take into account any of the more recent work on understanding probability. It used coins and dice. Big yawn. It wastes a lot of time. It was ok. I do like that you have the interactive transcript so you can find your way around.

It dawned on me that nowhere do you actually talk about what probability is. You seem to assume that the students already know that. In the very start of the first video it says,

“What I want to do in this video is give you at least a basic overview of probability. Probability, a word that you’ve probably heard a lot of and you are probably just a little bit familiar with it. Hopefully this will get you a little deeper understanding.”

Later in the video there is a section on the idea of large numbers of repetitions, which is one way of understanding probability. But it really is a bit skimpy on why anyone would want to find or estimate a probability, and what the values actually mean. But it was ok.

The first video was about single instances – one toss of a coin or one roll of a die. Then the second video showed you how to answer the questions in the exercises, which involved two dice. This seemed ok, if rather a sudden jump from the first video. Sadly both of these examples perpetuate the common misconception that if there are, say, 6 alternative outcomes, they will necessarily be equally likely.

## Exercises

Then we get to some exercises called “Probability Space” , which is not an enormously helpful heading. But my main quest was to have a go at the exercises, so that is what I did. And that was not a good thing. The exercises were not stepped, but started right away with an example involving two dice and the phrase “at least one of”. There was meant to be a graphic to help me, but instead I had the message “scratchpad not available”. I will summarise my concerns about the exercises at the end of my letter. I clicked on a link to a video that wasn’t listed on the left, called Probability Space and got a different kind of video.

This video was better in that it had moving pictures and a script. But I have problems with gambling in videos like this. There are some cultures in which gambling is not acceptable. The other problem I have is with the term  “exact probability”, which was used several times. What do we mean by “exact probability”? How does he know it is exact? I think this sends the wrong message.

Then on to the next videos which were worked examples, entitled “Example: marbles from a bag, Example: Picking a non-blue marble, Example: Picking a yellow marble.” Now I understand that you don’t want to scare students with terminology too early, but I would have thought it helpful to call the second one, “complementary events, picking a non-blue marble”. That way if a student were having problems with complementary events in exercises from school, they could find their way here. But then I’m not sure who your audience is. Are you sure who your audience is?

The first marble video was ok, though the terminology was sloppy.

The second marble video, called “Example: picking a non-blue marble”, is glacially slow. There is a point, I guess in showing students how to draw a bag and marbles, but… Then the next example is of picking numbers at random. Why would we ever want to do this? Then we come to an example of circular targets. This involves some problem-solving regarding areas of circles, and cancelling out fractions including pi. What is this about? We are trying to teach about probablity so why have you brought in some complication involving the area of a circle?

The third marble video attempts to introduce the idea of events, but doesn’t really. By trying not to confuse with technical terms, the explanation is more confusing.

Now onto some more exercises. The Khan model is that you have to get 5 correct in a row in order to complete an exercise. I hope there is some sensible explanation for this, because it sure would drive me crazy to have to do that. (As I heard expressed on Twitter)

## What are circular targets doing in with basic probability?

The first example is a circular target one.  I SO could not be bothered working out the area stuff so I used the hints to find the answer so I could move onto a more interesting example. The next example was finding the probability of a rolling a 4 from a fair six sided die. This is trivial, but would have been not a bad example to start with. Next question involve three colours of marbles, and finding the probability of not green. Then another dart-board one. Sigh. Then another dart board one. I’m never going to find out what happens if I get five right in a row if I don’t start doing these properly. Oh now – it gave me circumference. SO can’t be bothered.

And that was the end of Basic probability. I never did find out what happens if I get five correct in a row.

## Venn diagrams

The next topic is called “Venn diagrams and adding probabilities “. I couldn’t resist seeing what you would do with a Venn diagram. This one nearly reduced me to tears.

As you know by now, I have an issue with gambling, so it will come as no surprise that I object to the use of playing cards in this example. It makes the assumption that students know about playing cards. You do take one and a half minutes to explain the contents of a standard pack of cards.  Maybe this is part of the curriculum, and if so, fair enough. The examples are standard – the probability of getting a Jack of Hearts etc. But then at 5:30 you start using Venn diagrams. I like Venn diagrams, but they are NOT good for what you are teaching at this level, and you actually did it wrong. I’ve put a comment in the feedback section, but don’t have great hopes that anything will change. Someone else pointed this out in the feedback two years ago, so no – it isn’t going to change.

This diagram is misleading, as is shown by the confusion expressed in the questions from viewers. There should be a green 3, a red 12, and a yellow 1.

Now Venn diagrams seem like a good approach in this instance, but decades of experience in teaching and communicating complex probabilities has shown that in most instances a two-way table is more helpful. The table for the Jack of Hearts problem would look like this:

 Jacks Not Jacks Total Hearts 1 12 13 Not Hearts 3 36 39 Total 4 48 52

(Any teachers reading this letter – try it! Tables are SO much easier for problem solving than Venn diagrams)

But let’s get down to principles.

## The principles of instruction that KA have not followed in the examples:

• Start easy and work up
• Be interesting in your examples – who gives a flying fig about two dice or random numbers?
• Make sure the hardest part of the question is the thing you are testing. This is particularly violated with the questions involving areas of circles.
• Don’t make me so bored that I can’t face trying to get five in a row and not succeed.

## My point

Yes, I do have one. Mr Khan you clearly can’t be stopped, so can you please get some real teachers with pedagogical content knowledge to go over your materials systematically and make them correct. You have some money now, and you owe it to your benefactors to GET IT RIGHT. Being flippant and amateurish is fine for amateurs but you are now a professional, and you need to be providing material that is professionally produced. I don’t care about the production values – keep the stammers and “lellows” in there if you insist. I’m very happy you don’t have background music as I can’t stand it myself. BUT… PLEASE… get some help and make your videos and exercises correct and pedagogically sound.

Dr Nic

PS – anyone else reading this letter, take a look at the following videos for mathematics.

And of course I think my own Statistics Learning Centre videos are pretty darn good as well.

Another Open Letter to Sal ( I particularly like the comment by Michael Paul Goldenberg)

Breaking the cycle (A comprehensive summary of the responses to criticism of Khan

# The importance of being wrong

## We don’t like to think we are wrong

One of the key ideas in statistics is that sometimes we will be wrong. When we report a 95% confidence interval, we will be wrong 5% of the time. Or in other words, about 1 in 20 of 95% confidence intervals will not contain the population parameter we are attempting to estimate. That is how they are defined. The thing is, we always think we are part of the 95% rather than the 5%. Mostly we will be correct, but if we do enough statistical analysis, we will almost definitely be wrong at some point. However, human nature is such that we tend to think it will be someone else. There is also a feeling of blame associated with being wrong. The feeling is that if we have somehow missed the true value with our confidence interval, it must be because we have made a mistake. However, this is not true. In fact we MUST be wrong about 5% of the time, or our interval is too big, and not really a 95% confidence interval.

The term “margin of error” appears with increasing regularity as elections approach and polling companies are keen to make money out of sooth-saying. The common meaning of the margin of error is half the width of a 95% confidence interval. So if we say the margin of error is 3%, then about one time in twenty, the true value of the proportion will actually be more than 3% away from the reported sample value.

What doesn’t help is that we seldom do know if we are correct or not. If we knew the real population value we wouldn’t be estimating it. We can contrive situations where we do know the population but pretend we don’t. If we do this in our teaching, we need to be very careful to point out that this doesn’t normally happen, but does in “classroom world” only. (Thanks to MD for this useful term.) General elections can give us an idea of being right or wrong after the event, but even then the problem of non-sampling error is conflated with sampling error. When opinion polls turn out to miss the mark, we tend to think of the cause as being due to poor sampling, or people changing their minds, or all number of imaginative explanations rather than simple, unavoidable sampling error.

So how do we teach this in such a way that it goes beyond school learning and is internalised for future use as efficient citizens?

## Teaching suggestions

I have two suggestions. The first is a series of True/False statements that can be used in a number of ways. I have them as part of on-line assessment, so that the students are challenged by them regularly. They could be well used in the classroom as part of a warm-up exercise at the start of a lesson. Students can write their answers down or vote using hands.

Here are some examples of True/False statements (some of which could lead to discussion):

1. You never know if your confidence interval contains the true population value.
2. If you make your confidence interval wide enough you can be sure that you contain the true population value.
3. A confidence interval tells us where we are pretty sure the sample statistic lies.
4. It is better to have a narrow confidence interval than a wide one, as it gives us more certain information, even though it is more likely to be wrong.
5. If your study involves twenty confidence intervals, then you know that exactly one of them will be wrong.
6. If a confidence interval doesn’t contain the true population value, it is because it is one of the 5% that was calculated incorrectly.

## Experiential exercise

The other teaching suggestion is for an experiential exercise. It requires a little set up time.

Make a set of cards for students with numbers on them that correspond to the point estimate of a proportion, or a score that will lead to that. (Specifications for a set of 35 cards representing the results from a proportion of 0.54 and 25 trials is given below).

Introduce the exercise as follows:
“I have a computer game, and have set the ratio of wins to losses at a certain value. Each of you has played 25 times, and the number of wins you have obtained will be on your card. It is really important that you don’t look at other people’s cards.”

Hand them out to the students. (If you have fewer than 35 in your class, it might be a good idea to make sure you include the cards with 8 and 19 in the set you use – sometimes it is ok to fudge slightly to teach a point.)
“Without getting information from anyone else, write down your best estimate of the true proportion of wins to losses in the game. Do you think you are correct? How close do you think you are to the true value?”

They will need to divide the number of wins by 25, which should not lead to any computational errors! The point is that they really can’t know how close their estimate is to the true value – and what does “correct” mean?

Then work out the margin of error for a sample of size 25, which in this case is estimated at 20%. Get the students to calculate their 95% confidence intervals, and decide if they have the interval that contains the true population value. Get them to commit one way or the other.

Now they can talk to each other about the values they have.

There are several ways you can go from here. You can tell them what the population proportion was from which the numbers were drawn (0.54). They can then see that most of them had confidence intervals that included the true value, and some didn’t. Or you can leave them wondering, which is a better lesson about real life. Or you can do one exercise where you do tell them and one where you don’t.

This is an area where probability and statistics meet. You could make a nice little binomial distribution problem out of being correct in a number of confidence intervals. There are potential problems with independence, so you need to be a bit careful with the wording. For example: Fifteen  students undertake separate statistical analyses on the topics of their choice, and construct 95% confidence intervals. What is the probability that all the confidence intervals are correct, in that they do contain the estimated population parameter? This is well modelled by a binomial distribution with n =15 and p=0.05. P(X=0)=0.46. And another interesting idea – what is the probability that two or more are incorrect? 0.17 is the answer. So there is a 17% chance that more than one of the confidence intervals does not contain the population parameter of interest.

This is an area that needs careful teaching, and I suspect that some teachers have only a sketchy understanding of the idea of confidence intervals and margins of error. It is so important to know that statistical results are meant to be wrong some of the time.

Data for the 35 cards:

 Number on card 8 9 10 11 12 13 14 15 16 17 18 19 Number of cards 1 1 2 3 5 5 6 5 3 2 1 1

# Parts and whole

The whole may be greater than the sum of the parts, but the whole still needs those parts. A reflective teacher will think carefully about when to concentrate on the whole, and when on the parts.

## Golf

If you were teaching someone golf, you wouldn’t spend days on a driving range, never going out on a course. Your student would not get the idea of what the game is, or why they need to be able to drive straight and to a desired length. Nor would it be much fun! Similarly if the person only played games of golf it would be difficult for them to develop their game. Practice driving and putting is needed.  A serious student of golf would also read and watch experts at golf.

## Music

Learning music is similar. Anyone who is serious about developing as a musician will spend a considerable amount of time developing their technique and their knowledge by practicing scales, chords and drills. But at the same time they need to be playing full pieces of music so that they feel the joy of what they are doing. As they play music, as opposed to drill, they will see how their less-interesting practice has helped them to develop their skills. However, as they practice a whole piece, they may well find a small part that is tripping them up, and focus for a while on that. If they play only the piece as a whole, it is not efficient use of time. A serious student of music will also listen to and watch great musicians, in order to develop their own understanding and knowledge.

## Benefits of study of the whole and of the parts

In each of these examples we can see that there are aspects of working with the whole, and aspects of working with the parts. Study of the whole contributes perspective and meaning to study, and helps to tie things together. It helps to see where they have made progress. Study of the parts isolates areas of weakness, develops skills and saves time in practice, thus being more efficient.

It is very important for students to get an idea of the purpose of their study, and where they are going. For this reason I have written earlier about the need to see the end when starting out in a long procedure such as a regression or linear programming model.

It is also important to develop “statistical muscle memory” by repeating small parts of the exercise over and over until it is mastered. Practice helps people to learn what is general and what is specific in the different examples.

# Teaching conditional probability

We are currently developing a section on probability as part of our learning materials. A fundamental understanding of probability and uncertainty are essential to a full understanding of inference. When we look at statistical evidence from data, we are holding it up against what we could reasonably expect to happen by chance, which involves a probability model. Probability lies in the more mathematical area of the study of statistics, and has some fun problem-solving aspects to it.

A popular exam question involves conditional probability. We like to use a table approach to this as it avoids many of the complications of terminology. I still remember my initial confusion over the counter-intuitive expression P(A|B) which means the probability that an object from subset B has the property of A. There are several places where students can come unstuck in Bayesian review, and the problems can take a long time. We can liken solving a conditional probability problem to a round of golf, or a long piece of music. So what we do in teaching is that first we take the students step by step through the whole problem. This includes working out what the words are saying, putting the known values into a table, calculating the unknown values in the table, and the using the table to answer the questions involving conditional probability.

Then we work on the individual steps, isolating them so that students can get sufficient practice to find out what is general and what is specific to different examples. As we do this we endeavour to provide variety such that students do not work out some heuristic based on the wording of the question, that actually stops them from understanding. An example of this is that if we use the same template each time, students will work out that the first number stated will go in a certain place in the table, and the second in another place etc. This is a short-term strategy that we need to protect them from in careful generation of questions.

As it turns out students should already have some of the necessary skills. When we review probability at the start of the unit, we get students to calculate probabilities from tables of values, including conditional probabilities. Then when they meet them again as part of the greater whole, there is a familiar ring.

Once the parts are mastered, the students can move on to a set of full questions, using each of the steps they have learned, and putting them back into the whole. Because they are fluent in the steps, it becomes more intuitive to put the whole back together, and when they meet something unusual they are better able to deal with it.

## Starting a course in Operations Research/Management Science

It is interesting to contemplate what “the whole” is, with regard to any subject. In operations research we used to begin our first class, like many first classes, talking about what management science/operations research is. It was a pretty passive sort of class, and I felt it didn’t help as first-year university students had little relevant knowledge to pin the ideas on. So we changed to an approach that put them straight into the action and taught several weeks of techniques first. We started with project management and taught critical path. Then we taught identifying fixed and variable costs and break-even analysis. The next week was discounting and analysis of financial projects. Then for a softer example we looked at multi-criteria decision-making, using MCDM. It tied back to the previous week by taking a different approach to a decision regarding a landfill. Then we introduced OR/MS, and the concept of mathematical modelling. By then we could give real examples of how mathematical models could be used to inform real world problems. It was helpful to go from the concrete to the abstract. This was a much more satisfactory approach.

So the point is not that you should always start with the whole and then do the parts and then go back to the whole. The point is that a teacher needs to think carefully about the relationship between the parts and the whole, and teach in a way that is most helpful.

# Let’s hear it for the Triangular Distribution!

Dr Nic meets Telly monster on the set of Sesame Street

Telly monster is my favourite character on Sesame Street, and a few years ago I was lucky enough to actually meet him. This morning I was delighted to find out from my resident Sesame Street expert that Telly monster is a triangle lover. I too am becoming a triangle lover.

I have learned recently about the triangular distribution. For some reason it is in the New Zealand curriculum and I wondered why, never having used it or seen it in any statistics textbook. I still don’t know the official motivation for including it, but it is a really good idea. The triangular distribution seems to be a useful teaching tool. I say “seems to be” as I haven’t actually used it in a class, but I can see the potential for some very good exercises and learning experiences.

There are three aspects of the triangular distribution that I find appealing.

## Multiple models for the same scenario

Using the triangular distribution alongside the normal distribution encourages the idea that the distributions are models of a real-life process. Like many curricula, the previous NZ curriculum included binomial, Poisson and normal distributions. Any questions of what model to use in a specific scenario were determined pretty much by the form of the story. If the story involved continuous data it pretty much had to be “normally distributed”.  This could imbed a false impression that all continuous data was appropriately modelled by the normal distribution.

A nice learning experience is to take some real data such as weights of lemons from a tree, and see how well that is modelled by a normal distribution. This can be enriched by also modelling this as a triangular distribution and seeing how well the two compare. A further extension would be to use a uniform distribution model of the same data. The beauty of this exercise is that it reinforces the idea that a distribution is a model of reality, and that there are different models that may be more or less appropriate for different data and circumstances.

## Different characteristics

I find contrasts are helpful for teaching. The triangular distribution provides a nice contrast with the normal distribution in a number of ways.

First is the requirements for specification. The triangular distribution is specified by the maximum, minimum and peak values. When we are making a subjective estimate, such as for completion time for a task, these are three easily pictured amounts – the longest time, the shortest time, and the most likely time for completion. This compares with the normal distribution, for which we need the mean and standard deviation, which would often be drawn from a sample.

The triangular distribution has a finite range, bounded by the maximum and minimum values. In a triangular distribution we can specify, for instance, that the results of a test will lie between 0 and 10. Modelling such a situation with a normal distribution can give results outside the range, as it theoretically goes to infinity in both directions.

The triangular distribution is not symmetric. It can be, but it is not a requirement. Where we have severely skewed data, it may well be that the triangular distribution is a better model than the normal distribution. This helps us when teaching the use of the normal distribution. The contrast is helpful.

## Area under the pdf is the probability

The idea that the probability of a continuous distribution is the area under the probability mass function is a difficult one for many students to get their heads around. One way to teach this is to start with a discrete distribution and then cut it up into finer and finer points. But then finding the actual area is problematic. With the normal distribution, the computation is hidden in the calculator, spreadsheet or tables. With the uniform distribution, the computation is trivial and can seem contrived. But areas under the graph in the triangular distribution can be calculated, and the exercise is not trivial. It also very nicely shows how the pdf is not going to give them the probability of a single value.

So there you have it – three important lessons in one tidy little triangle-shaped package.

And to help you use the triangular distribution in your teaching, we have this handout: Notes on Triangle Distributions which you are welcome to use, so long as you leave our branding on it. And if you want your students to have some great practice exercises, they can always join our course!

# Conceptualising Probability

The problem with probability is that it doesn’t really exist. Certainly it never exists in the past.

Probability is an invention we use to communicate our thoughts about how likely something is to happen. We have collectively agreed that 1 is a certain event and 0 is impossible. 0.5 means that there is just as much chance of something happening as not. We have some shared perception that 0.9 means that something is much more likely to happen than to not happen. Probability is also useful for when we want to do some calculations about something that isn’t certain. Often it is too hard to incorporate all uncertainty, so we assume certainty and put in some allowance for error.

Sometimes probability is used for things that happen over and over again, and in that case we feel we can check to see if our predication about how likely something is to happen was correct. The problem here is that we actually need things to happen a really big lot of times under the same circumstances in order to assess if we were correct. But when we are talking about the probability of a single event, that either will or won’t happen, we can’t test out if we were right or not afterwards, because by that time it either did or didn’t happen. The probability no longer exists.

Thus to say that there is a “true” probability somewhere in existence is rather contrived. The truth is that it either will happen or it won’t. The only way to know a true probability would be if this one event were to happen over and over and over, in the wonderful fiction of parallel universes. We could then count how many times it would turn out one way rather than another. At which point the universes would diverge!

However, for the interests of teaching about probability, there is the construct that there exists a “true probability” that something will happen.

What prompted these musings about probability was exploring the new NZ curriculum and companion documents, the Senior Secondary Guide and nzmaths.co.nz.

In Level 8 (last year of secondary school) of the senior secondary guide it says, “Selects and uses an appropriate distribution to solve a problem, demonstrating understanding of the relationship between true probability (unknown and unique to the situation), model estimates (theoretical probability) and experimental estimates.”

And at NZC level 3 (years 5 and 6 at Primary school!) in the Key ideas in Probability it talks about “Good Model, No Model and Poor Model” This statement is referred to at all levels above level 3 as well.

I decided I needed to make sense of these two conceptual frameworks: true-model-experimental and good-poor-no, and tie it to my previous conceptual framework of classical-frequency-subjective.

Here goes!

## Delicious Mandarins

Let’s make this a little more concrete with an example. We need a one-off event. What is the probability that the next mandarin I eat will be delicious? It is currently mandarin season in New Zealand, and there is nothing better than a good mandarin, with the desired combination of sweet and sour, and with plenty of juice and a good texture. But, being a natural product, there is a high level of variability in the quality of mandarins, especially when they may have parted company with the tree some time ago.

There are two possible outcomes for my future event. The mandarin will be delicious or it will not. I will decide when I eat it. Some may say that there is actually a continuum of deliciousness, but for now this is not the case. I have an internal idea of deliciousness and I will know. I think back to my previous experience with mandarins. I think about a quarter are horrible, a half are nice enough and about a quarter are delicious (using the Dr Nic scale of mandarin grading). If the mandarin I eat next belongs to the same population as the ones in my memory, then I can predict that there is a 25% probability that the mandarin will be delicious.

The NZ curriculum talks about “true” probability which implies that any value I give to the probability is only a model. It may be a model based on empirical or experimental evidence. It can be based on theoretical probabilities from vast amounts of evidence, which has given us the normal distribution. The value may be only a number dredged up from my soul, which expresses the inner feeling of how likely it is that the mandarin will be delicious, based on several decades of experience in mandarin consumption.

## More examples

Let us look at some more examples:

What is the probability that:

• I will hear a bird on the way to work?
• the flight home will be safe?
• it will be raining when I get to Christchurch?
• I will get a raisin in my first spoonful of muesli?
• I will get at least one raisin in half of my spoonfuls of muesli?
• the shower in my hotel room will be enjoyable?
• I will get a rare Lego ® minifigure next time I buy one?

All of these events are probabilistic and have varying degrees of certainty and varying degrees of ease of modelling.

 Easy to model Hard to model Unlikely Get a rare Lego ® minifigure Raining in Christchurch No idea Raisin in half my spoonfuls Enjoyable shower Likely Raisin in first spoonful Bird, safe flight home

And as I construct this table I realise also that there are varying degrees of importance. Except for the flight home, none of those examples matter. I am hoping that a safe flight home has a probability extremely close to 1. I realise that there is a possibility of an incident. And it is difficult to model. But people have modelled air safety and the universal conclusion is that it is safer than driving. So I will take the probability and fly.

# Conceptual Frameworks

How do we explain the different ways that probability has been described? I will now examine the three conceptual frameworks I introduced earlier, starting with the easiest.

This is found in some form in many elementary college statistics text books. The traditional framework has three categories –classical or “a priori”, frequency or historical, and subjective.

Classical or “a priori” – I had thought of this as being “true” probability. To me, if there are three red and three white Lego® blocks in a bag and I take one out without looking, there is a 50% chance that I will get a red one. End of story. How could it be wrong? This definition is the mathematically interesting aspect of probability. It is elegant and has cool formulas and you can make up all sorts of fun examples using it. And it is the basis of gambling.

Frequency or historical – we draw on long term results of similar trials to gain information. For example we look at the rate of germination of a certain kind of seed by experiment, and that becomes a good approximation of the likelihood that any one future seed will germinate. And it also gives us a good estimate of what proportion of seeds in the future will germinate.

Subjective – We guess! We draw on our experience of previous similar events and we take a stab at it. This is not seen as a particularly good way to come up with a probability, but when we are talking about one off events, it is impossible to assess in retrospect how good the subjective probability estimate was. There is considerable research in the field of psychology about the human ability or lack thereof to attribute subjective probabilities to events.

In teaching the three part categorisation of sources of probability I had problems with the probability of rain. Where does that fit in the three categories? It uses previous experimental data to build a model, and current data to put into the model, and then a probability is produced. I decided that there is a fourth category, that I called “modelled”. But really that isn’t correct, as they are all models.

## NZ curriculum terminology

So where does this all fit in the New Zealand curriculum pronouncements about probability? There are two conceptual frameworks that are used in the document, each with three categories as follows:

## True, modelled, experimental

In this framework we start with the supposition that there exists somewhere in the universe a true probability distribution. We cannot know this. Our expressions of probability are only guesses at what this might be. There are two approaches we can take to estimate this “truth”. These two approaches are not independent of each other, but often intertwined.

One is a model estimate, based on theory, such as that the probability of a single outcome is the number of equally likely ways that it can occur over the number of possible outcomes. This accounts for the probability of a red brick as opposed to a white brick, drawn at random. Another example of a modelled estimate is the use of distributions such as the binomial or normal.

In addition there is the category of experimental estimate, in which we use data to draw conclusions about what it likely to happen. This is equivalent to the frequency or historical category above. Often modelled distributions use data from an experiment also. And experimental probability relies on models as well.  The main idea is that neither the modelled nor the experimental estimate of the “true” probability distribution is the true distribution, but rather a model of some sort.

## Good model, poor model, no model

The other conceptual framework stated in the NZ curriculum is that of good model, poor model and no model, which relates to fitness for purpose. When it is important to have a “correct” estimate of a probability such as for building safety, gambling machines, and life insurance, then we would put effort into getting as good a model as possible. Conversely, sometimes little effort is required. Classical models are very good models, often of trivial examples such as dice games and coin tossing. Frequency models aka experimental models may or may not be good models, depending on how many observations are included, and how much the future is similar to the past. For example, a model of sales of slide rules developed before the invention of the pocket calculator will be a poor model for current sales. The ground rules have changed. And a model built on data from five observations of is unlikely to be a good model. A poor model is not fit for purpose and requires development, unless the stakes are so low that we don’t care, or the cost of better fitting is greater than the reward.

I have problems with the concept of “no model”. I presume that is the starting point, from which we develop a model or do not develop a model if it really doesn’t matter. In my examples above I include the probability that I will hear a bird on the way to work. This is not important, but rather an idle musing. I suspect I probably will hear a bird, so long as I walk and listen. But if it rains, I may not. As I am writing this in a hotel in an unfamiliar area I have no experience on which to draw. I think this comes pretty close to “no model”. I will take a guess and say the probability is 0.8. I’m pretty sure that I will hear a bird. Of course, now that I have said this, I will listen carefully, as I would feel vindicated if I hear a bird. But if I do not hear a bird, was my estimate of the probability wrong? No – I could assume that I just happened to be in the 0.2 area of my prediction. But coming back to the “no model” concept – there is now a model. I have allocated the probability of 0.8 to the likelihood of hearing a bird. This is a model. I don’t even know if it is a good model or a poor model. I will not be walking to work this way again, so I cannot even test it out for the future, and besides, my model was only for this one day, not for all days of walking to work.

So there you have it – my totally unscholarly musings on the different categorisations of probability.

## What are the implications for teaching?

We need to try not to perpetuate the idea that probability is the truth. But at the same time we do not wish to make students think that probability is without merit. Probability is a very useful, and at times highly precise way of modelling and understanding the vagaries of the universe. The more teachers can use language that implies modelling rather than rules, the better. It is common, but not strictly correct to say, “This process follows a normal distribution”. As Einstein famously and enigmatically said, “God does not play dice”. Neither does God or nature use normal distribution values to determine the outcomes of natural processes. It is better to say, “this process is usefully modelled by the normal distribution.”

We can have learning experiences that help students to appreciate certainty and uncertainty and the modelling of probabilities that are not equi-probable. Thanks to the overuse of dice and coins, it is too common for people to assess things as having equal probabilities. And students need to use experiments.  First they need to appreciate that it can take a large number of observations before we can be happy that it is a “good” model. Secondly they need to use experiments to attempt to model an otherwise unknown probability distribution. What fun can be had in such a class!

But, oh mathematical ones, do not despair – the rules are still the same, it’s just the vigour with which we state them that has changed.

Comment away!

## Post Script

In case anyone is interested, here are the outcomes which now have a probability of 1, as they have already occurred.

• I will hear a bird on the way to work? Almost the minute I walked out the door!
• the flight home will be safe? Inasmuch as I am in one piece, it was safe.
• it will be raining when I get to Christchurch? No it wasn’t
• I will get a raisin in my first spoonful of muesli? I did
• I will get at least one raisin in half of my spoonfuls of muesli? I couldn’t be bothered counting.
• the shower in my hotel room will be enjoyable? It was okay.
• I will get a rare Lego minifigure next time I buy one? Still in the future!

# Why engineers and poets need to know about statistics

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.

# So what about the poets?

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.