Experimental Design & Analysis Online

Provider business unit: Statistical Consulting Unit »

The Statistical Consulting Unit runs a number of courses throughout the academic year. The objective of the courses is to promote sound statistical thinking and an awareness of appropriate modern statistical practice, leading to informative analyses. This is of particular value to students who utilise statistical concepts and methods in research theses as well as those seeking to enhance their wider statistical understanding. The courses are:

  • are designed for research students and ANU staff who have no or minimal training and knowledge of statistical analysis
  • use a case-study, problem oriented approach to teaching
  • do not require sophisticated mathematical skills
  • provide an introduction to modern statistical practice
  • provide an introduction to the underlying modern statistical thinking and concepts used in many analyses.


This course lays down the foundations of good experimental design. Students should allow about 20 hours for this course.  It will help answer questions like:

  • Is it better to have more treatments or more replicates?
  • How do I choose the treatment for each experimental unit?
  • What is the best layout for the trial in the field or laboratory?
  • How many experimental units (what sample size) do I need?

Target participants

Students and researchers who are interested in the statistical issues in experimental design and the analysis of data from designed experiments or studies with several factors.

Course content

  • Developing specific objectives for a study within a broad research project;
  • The basic principles of experimental design: randomisation, replication, blocking and  local control;
  • Advantages of factorial treatment designs;
  • Randomisation as the basis for the analysis;
  • Common designs: completely randomised, randomised complete block, split plot experiments;
  • Organising data into a form suitable for analysis using a statistics package;
  • Hypothesis testing via the analysis of variance table;
  • Estimation of means and standard errors;
  • Interpretation of output from a statistics package used for analysing one of the above designs.

Please click here for detailed course content of Experimental Design & Analysis.

Assumed knowledge

It is assumed that course participants have completed Introductory Statistics Online or equivalent and so have familiarity with:

  • The concept of variation;
  • Graphical methods for displaying the distribution of data;
  • Confidence for a mean;
  • Interpreting the P-value from a hypothesis test.


This course is free to ANU Staff & Students (honours & research only).

Registrations are open year-round. Please register and follow the prompts for enrolment.

Each course provides a practical introduction to various statistical methods. There will be plenty of opportunity for discussion with course leaders on appropriate methods of analysing data, and help will be provided with interpreting results.

Understanding the basic concepts and issues makes the consultation process more efficient and effective. We recommend that all students who intend involving the SCU in their project attend some relevant courses.