# concepts

16 September 2013

## How to learn statistics (Part 2)

Some more help (preaching?) for students of statistics Last week I outlined the first five principles to help people to learn and study statistics. They focussed on how you need to practise in order to be good at statistics and you should not wait until you understand it completely before you start applying. I sometimes call this suspending disbelief. Next I talked about the importance of context in a statistical investigation, which is one of the ways that statistics is different from pure mathematics. And finally I stressed the importance of technology as a tool, not only for doing the […]
26 August 2013

## Statistics is not beautiful (sniff)

Statistics is not really elegant or even fun in the way that a mathematics puzzle can be. But statistics is necessary, and enormously rewarding. I like to think that we use statistical methods and principles to extract truth from data. This week many of the high school maths teachers in New Zealand were exhorted to take part in a Stanford MOOC about teaching mathematics. I am not a high school maths teacher, but I do try to provide worthwhile materials for them, so I thought I would take a look. It is also an opportunity to look at how people […]
22 July 2013

## Conceptualising Probability

The problem with probability is that it doesn’t really exist. Certainly it never exists in the past [once we know the outcome]. (Looking for the Experimental Design post linked from our Newsletter? Use this link.) 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. […]
8 July 2013

## Oh Ordinal data, what do we do with you?

What can you do with ordinal data? Or more to the point, what shouldn’t you do with ordinal data? First of all, let’s look at what ordinal data is. It is usual in statistics and other sciences to classify types of data in a number of ways. In 1946, Stanley Smith Stevens suggested a theory of levels of measurement, in which all measurements are classified into four categories, Nominal, Ordinal, Interval and Ratio. This categorisation is used extensively, and I have a popular video explaining them. (Though I group Interval and Ratio together as there is not much difference in […]
27 May 2013

## Probability and Deity

Our perception of chance affects our worldview There are many reasons that I am glad that I majored in Operations Research rather than mathematics or statistics. My view of the world has been affected by the OR way of thinking, which combines hard and soft aspects. Hard aspects are the mathematics and the models, the stuff of the abstract world. Soft aspects relate to people and the reality of the concrete world.  It is interesting that concrete is soft! Operations Research uses a combination of approaches to aid in decision making. My mentor was Hans Daellenbach, who was born and […]
6 May 2013

## Teaching a service course in statistics

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 […]
22 April 2013

## Is statistical enquiry a cycle?

What is the statistical enquiry cycle and why is it a cycle? Is it really a cycle? The New Zealand curriculum for Mathematics and statistics was recently held up as an example of good practice with regard to statistics. Yay us! In New Zealand the learning of statistics starts at the beginning of schooling and is part of the curriculum right through the school years. Statistics is developed as a discipline alongside mathematics, rather than as a subset of it. There are mathematics teachers who view this as an aberration, and believe that when this particular fad is over statistics […]
1 April 2013

## Context – if it isn't fun…

The role of context in statistical analysis The wonderful advantage of teaching statistics is the real-life context within which any applicaton must exist. This can also be one of the difficulties. Statistics without context is merely the mathematics of statistics, and is sterile and theoretical.  The teaching of statistics requires real data. And real data often comes with a fairly solid back-story. One of the interesting aspects for practicing statisticians, is that they can find out about a wide range of applications, by working in partnership with specialists. In my statistical and operations research advising I have learned about a […]
4 March 2013

## Shibboleth, Mixolydian, Heteroscedasticity – and Kipling

All areas of human endeavour have specific language. Cricket commentators, art critics and wines buff make this very obvious. Mixolydian My son, who is blind, autistic and plays the piano like an angel, is studying Jazz, and I’m helping him. You can see him on his YouTube channel . There is a specific language around Jazz, and I’m not talking about ‘scat’. (Hmm just realised the other meaning for that word!) In the Jazz course they use words like Mixolydian, Chromatisism, Quartal Harmony…  I nod and smile. This language expresses ideas clearly and uniquely and is outside my comprehension. (Mixolydian is […]
25 February 2013

## Interpreting Scatterplots

Patterns, vocab and practice, practice, practice An important part of statistical analysis is being able to look at graphical representation of data, extract  meaning and make comments about it, particularly related to the context. Graph interpretation is a difficult skill to teach as there is no clear algorithm, such as mathematics teachers are used to teaching, and the answers are far from clear-cut. This post is about the challenges of teaching scatterplot interpretation, with some suggestions. When undertaking an investigation of bivariate measurement data, a scatterplot is the graph to use. On a scatterplot we can see what shape the […]