I’m currently putting together a course on numerical and statistical literacy aimed at all undergraduates at Edinburgh. This is a challenge, not least because it means that I cannot assume any particular substantive academic discipline as a context. I’ve also been going round in a few circles over what to go into first. Start with randomness and then the idea of a variable distribution and then correlation? Or start with description and the nature of variables? Text for the course will be Dilnot and Blastland’s ‘Tiger that Isnt’. The ‘learning outcomes’ I’ve come up with are below: all suggestions about additions, deletions or amendments gratefully received… Currently it is a very long list: probably too long, but what to leave out??
Understand what a variable is, what is meant by its distribution, and some of the ways in which the latter can be described and summarised.
Recognise and carry out simple manipulation of proportions, fractions, decimals, and percentages.
Understand rates and rates of change, and their expression by logarithms.
Use procedures of informal estimation to check the orders of magnitude of quantities used in reports (including academic or scientific output, policy documents or the mass media) and to avoid spurious accuracy.
Understand the meaning of randomness and of the independence of events or states.
Recognise a ‘Normal’ or Gaussian distribution.
Understand probability and risk as ways of measuring uncertainty.
Undertake simple calculations of cumulative and conditional probability
Understand the distinction between absolute and relative risks, and perform simple calculations of risk using natural frequencies and Bayes rule.
Understand the concept of correlation and its distinction from causation.
Know how to read, interpret, produce and present data in the form of contingency tables.
Understand the difference between experiment and observation
Understand the difference between observational and experimental control
Understand what is meant by a random sample and sampling fluctuation
Understand what a confidence interval is and how it is expressed
The distinction between significance and substance
The concept of regression to the mean and its implications
Understand how data can be visualised, including bar charts, histograms, box plots, scatterplots and Venn diagrams.
Be able to use Excel to record, store, manipulate and present numerical data.