# Teaching a service course in statistics

Most students who enrol in an initial course in statistics at university level do so because they have to. I did some research on attitudes to statistics in my entry level quantitative methods course, and fewer than 1% of the students had chosen to be in that course. This is a little demoralising, if you happen to think that statistics is worthwhile and interesting.

Teaching a service course in statistics is one of the great challenges of teaching. A “Service Course” is a course in statistics for students who are majoring in some other subject, such as Marketing or Medicine or Education. For some students it is a terminating course – they will never have to look at a p-value again (they hope). For some students it is the precursor to further applied statistics such as marketing research or biological research. Having said that, statistics for citizens is important and interesting and engaging if taught that way. And we might encourage some students to carry on.

Yet the teachers and textbook writers seem to do their best to remove the joy. Statistics is a difficult subject to understand. Often the way the instructor thinks is at odds with the way the students think and learn. The mathematical nature of the subject is invested with all sorts of emotional baggage.

Here are some of the challenges of teaching a statistics service course.

## Limited mathematical ability

It is important to appreciate how limited the mathematical understanding is of some of the students in service courses. In my first year quantitative methods course, I made sure my students knew basic algebra, including rearranging and solving equations. This was all done within a business context. Even elementary algebra was quite a stumbling block to some students, for whom algebra had been a bridge too far at school. There were students in a postgrad course I taught who were not sure which was larger, out of 0.05 and 0.1, and talked about crocodiles with regard to greater than and less than signs. And these were schoolteachers! Another senior maths teacher in that group had been teaching the calculation of confidence intervals, without actually understanding what they were.

The students are not like statisticians. Methods that worked to teach statisticians and mathematicians are unlikely to work for them. I wrote about this in my post about the Golden Rule, and how it applies at a higher level for teaching.

I realised a few years ago that I am not a mathematician. I do not have the ability to think in the abstract that is part of a true mathematician. Operations Research was my thing, because I was good at mathematics, but my understanding was concrete. This has been a surprising gift for me as a teacher, as it has meant that I can understand better what the students find difficult. Formulas do not tell them anything. Calculating by hand does not lead to understanding. It is from this philosophy that I approach the production of my videos. I am particularly pleased with my recent video about confidence intervals, which explains the ideas, with nary a formula in sight, but plenty of memorable images.

## Software

One of my more constantly accessed posts is Excel, SPSS, Minitab or R?. This consistent interest indicates that the course of software is a universal problem. People are very quick to say how evil Excel is, and I am under no illusions as to many of the shortcomings. The main point of my post was, however, that it depends on the class you are teaching.

As I have taught mainly business students, I still hold that for them, Excel is ideal. Not so much for the statistical aspects, but because they learn to use Excel. Last Saturday the ideas for today’s posts were just forming in my mind when the phone rang, and despite my realising it was probably a telemarketer (we have caller ID on our phone) I answered it. It was a nice young woman asking me to take part in a short survey about employment opportunities for women in the Christchurch Rebuild. After I’d answered the questions, explaining that I was redundant from the university because of the earthquakes and that I had taught statistics, she realised that I had taught her. (This is a pretty common occurrence for me in our small town-city – even when I buy sushi I am served by ex-students). So I asked her about her experience in my course, and she related how she would never have taken the course, but enjoyed it and passed. I asked about Excel, and she told me that she had never realised what you could do with Excel before, and now still used it. This is not an isolated incident. When students are taught Excel as a tool, they use it as a tool, and continue to do so after the course has ended.

When business students learn using Excel, it has the appearance of relevance. They are aware that spreadsheets are used in business. It doesn’t seem like time wasted. So I stand by my choice to use Excel. However if I were still teaching at University, I would also be using iNZight. And if I taught higher levels I would continue to use SPSS, and learn more about R.

## Textbooks

As I said in a previous post Statistics Textbooks suck out all the fun. Very few textbooks do no harm. I wonder if this site could provide a database of statistics texts and reviews. I would be happy to review textbooks and include them here. My favourite elementary textbook is, sadly, out of print. It is called “Taking the Fear out of Data Analysis”, by the fabulously named Adamantis Diamantopoulos and Bodo Schlegelmilch. It takes a practical approach, and has a warm, nurturing style. It lacks exercises. I have used extracts from it over the years. The choice of textbook, like the choice of software, is “horses for courses”, but I think there are some horses that should not be put anywhere near a course. I do wonder how many students use textbooks as anything other than a combination lucky charm and paper weight.

In comparison with the plethora of college texts of varying value, at high-school level the pickings for textbooks are thin. This probably reflects the newness of the teaching of statistics at high-school level. A major problem with textbooks is that they are so quickly out of date, and at school level it is not practical to replace class sets too often.

Perhaps the answer is online resources, which can be updated as needed, and are flexible and give immediate feedback. 😉

## Emotional baggage

I was less than gentle with a new acquaintance in the weekend. When asked about my business, I told him that I make on-line materials to help people teach and learn statistics. He proceeded to relate a story of a misplaced use of a percentage as a reason why he never takes any notice of statistics. I have tired of the “Lies, damned lies, and statistics” jibe and decided not to take it lying down. I explained that the world is a better place because of statistical analysis. Much research, including medical would not be possible in the absence of methods for statistical analysis. An understanding of the concepts of statistics is a vital part of intelligent citizenship, especially in these days of big and ubiquitous data.

I stopped at that point, but have pondered since. What is it that makes people so quick to denigrate the worth of statistics? I suspect it is ignorance and fear. They make themselves feel better about their inadequacies by devaluing the things they lack. Just a thought.

This is not an isolated instance. In fact I was so surprised when a lighthouse keeper said that statistics sounded interesting and wanted to know more, that I didn’t really know what to say next! You can read about that in a previous post. Statistics is an interesting subject – really!

But the students in a service course in statistics may well be in the rather large subset of humanity who have yet to appreciate the worth of the subject. They may even have fear and antipathy towards the subject, as I wrote about previously. Anxiety, fear and antipathy for maths, stats and OR.

People are less likely to learn if they have negative attitudes towards the subject. And when they do learn it may well be “learning to pass” rather than actual learning which is internalised.

## So what?

Keep the faith! Statistics is an important subject. Keep trying new things. If you never have a bad moment in your teaching, you are not trying enough new things. And when you hear from someone whose life was changed because of your teaching, there is nothing like it!

Nic, I completely agree with your comments here. With such service students, the maths is the last thing they need to be taught. Far more important are their ability to think about data in statistical terms and to interpret the results. I particularly agree with your point about Excel. I use this for my training courses, all of which are aimed at people in analytical roles but who have either not had a statistical education or have been scarred by it. More often that not, it is some of the tricks of Excel that people will highlight in their feedback. Since they are already using Excel, I think they find it easier to extend what they already know rather than learn something completely new. At some point they will need a more advanced software and for me, the availability these of Statistical Excel Add-ins is really important and RExcel has been a major breakthrough here in my opinion.

Hi Nicola,

Yes, the “Lies, damn lies and statistics” quote annoys the heck out of me also.

Apparently this quote goes back a long way – some people quote Twain, Twain was quoting Disraeli, but the quote originated before Disraeli. In fact, my understanding (and I could be wrong) is that the quote goes back to a time when the term ‘Statistics’ was more indicative of its original etymological origin, from the word ‘state’, and pertained to the interpretation of census data used by governments.

So perhaps a more correct translation of the original meaning of the quote

is:

“Lies, damn lies, and numbers quoted by governments”

🙂

The quote certainly has no relevance to the modern discipline of statistics, and it is always annoying when people repeat it, even in jest…

What also annoys is the idea that statistics is optional – that one can simple ignore the statistical complexities of a problem and just trust a naive interpretation of the data…

So true. I think we could do a whole post about annoying attitudes towards statistics.

From my very earliest experience of trying to support research scientists in their work, even as a very green and undeveloped statistician, it became clear to me that the majority of graduates have been traumatised by their early exposure to statistics. Responses ranged from disinterest, through depression, confusion and fear, all the way to outright aggression.

25 years on and I now spend much of my time teaching basic statistics courses to scientists, engineers, medics, economists and others who have discovered that they need statistics in their working lives. Overwhelmingly they are still reporting the same educational experiences.

My (untested) thesis is that this is because the subject is, or at least has been, taught by highly qualified academics who believe that statistics is a subset of mathematics. They start with probability & distribution theory, then go on to methods, being careful to include all of the important mathematical details, unfathomable terminology and other background only of relevance to other mathematical statisticians. Only then, when everyone in the class has lost the will to live, do they start to discuss some of the reasons why this stuff might be useful. This is undoubtedly a parody but there is enough truth in it to be recognisable to many.

It’s a tragedy for our subject and for the world, but hey, it pays my bills, so who am I to complain.

Useful and fun blog. I am not the worlds greatest statistician which I think makes it easier for me to teach it – I have to work at it. I’m a biologist and it is something of a tradition that we are the scientists that cant do maths very well. As I get older however, I’m getting more and more into stats.

I was a lecturer in statsitics for 20 years and am now an industrial statistician. It is very frustrating to find that scientists have attempted to do some serious statistics, only to get an entirely innappropriate solution. There must be tons of this published so that bad solutions are continually being promulgated, resulting in an enormous waste of resource.

The following is no doubt just repeating old views, so apologies, but it does bear saying again.

My view would be yes, teach them a bit about simple statistical analysis and variation and definitely some basic probability and distributions, but drive home that this will only give them a very basic starting point. And perhaps the main objective is to give them enough basic understanding so that they have a chance of getting some idea of what a statistician is talking about. If they have to solve complex problems associated with understanding their data, then a statistician is going to be needed at an early stage and he or she is going to use techniques that they cannot use, although they may be able to grasp some of it if they have the right basic equipment. What we must avoid is them thinking that they can apply quite sophisticated techniques, for they will surely not know what the pitfalls are, nor if their sources have made errors. I have examples of both, and these are pitfalls that can only be avoided by calling in an expert at an early stage.

Probably industrial statisticians should share their problems and solutions with academia (maybe this does go on, so tell me!) so that the students can be exposed to real problems and solutions (so here is something I should try to engage with, and something I remember being particularly difficult to do when I was on the other side of the fence 30 years ago. The right kind of problems never seemed to be available.) Of course, some such problems will require considerable investment of time for students to understand and we need to find a vehicle whereby this can be achieved. Other problems might be quite simple and then there will be those that use statistical techniques way beyond any non-specialist. The latter can usually be partly explained provided some basic models and distributions have been understood, but, perhaps more importantly, the fact that far more sophisticated tools will usually be needed is drummed in. Probably the trouble is that real problems are too fr removed from the degree material being taught. I don’t really know. Anyway, science is a team effort and the statistician is a vital team member. This cannot be over stressed! But I can say that the battle is still a very real one and it isn’t made any easier by the increasing complexities of the science that is being done and its need for ever more complex statistics.

Thanks for your comments. Absolutely agree. I doubt many of my first year students went on to use much of their statistics other than a basic awareness. (I know they use the Excel skills though!)

It would also be really helpful if statisticians and researchers would share their data, or subsets thereof with academia. Sometimes the simple solutions are enough.