The Opal™ Performance Measurement System

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Presentation Graphics

Cleveland, W S and Fisher, N I (1998),  “Good graphs for Better Business”. The Quality Magazine 7  (4),  64-68.



Are we on track with our target for market share? Is our installation process capable of meeting the industry benchmark? What things are causing most of the unhappiness with our staff? These are typical of the management problems that require timely and accurate information. They are also problems for which effective use of graphs can make a big difference. The good news is that graphical capabilities are now readily available in statistical, spreadsheet, and word-processing packages. The bad news is that much of this graphical capability produces graphs that can hide or seriously distort crucial information contained in the data.

For a long time, how to make a good graph was largely a matter of opinion. However, the last 20 years have seen the development of a set of principles for sound graphical construction, based on solid scientific research and experimentation. Good entry points to these principles are provided in the References. In this article, we look at a couple of common examples of applying these principles.

[Full PDF]

Statistics and Data Science Education

Detailed information about the International Data Science in Schools Project (IDSSP) can be found at

The purpose of this international collaborative project is to transform the way education in Data Science is carried out in the last two years of school, with two objectives:

  1. To ensure that school students acquire a sufficient understanding and appreciation of how data can be acquired and used to make decisions so that they can make informed judgments in their daily lives, as students and then as adults. In particular, we envisage future generations of lawyers, journalists, historians, and many others, leaving school with a basic understanding of how to work with data to make decisions in the presence of uncertainty, and how to interpret quantitative information presented to them in the course of their professional and personal activities.
  2. To instil in more scientifically able school students sufficient interest and enthusiasm for Data Science that they will seek to pursue tertiary studies in Data Science with a view of making a career in the area.

In both cases, we want to teach people how to Learn from Data.

Our goal is to provide the content for a pre-calculus course in Data Science that is fun to learn and fun to teach. A total of some 240 hours of instruction is envisaged. As a parallel development we will devise a program will enable teachers from a wide variety of backgrounds – basically any discipline that involves data, or mathematics teachers – to learn to present the course well. It is also planned to make the course available in a variety of modes of delivery.

The project will be carried out in two phases.

Phase 2:

In the Implementation phase, the curriculum frameworks devised in Phase 1 will provide the basis for developing resources to support courses in a variety of formats, suitable for different modes of delivery (classroom, MOOC, self-learning, etc).

The project involves computer scientists, statisticians, school teachers, curriculum experts and educators from Australia, Canada, England, Germany, New Zealand and the United States.

Framework documents are available from the IDSSP Framework page.


Directional Statistics

The following items can be downloaded as the Directional Statistics zipped bundle [download bundle]



Fisher, N I & Trewin, D (2020), “A proposal to enhance Australia’s capability to manage epidemics — The critical importance of expert statistical input”.


Executive summary

Given the high level of global mobility, pandemics are likely to be more frequent, and with potentially devastating consequences for the Australian community and its way of life. Whilst parts of Australia are experiencing a second wave of COVID-19, the country is in relatively better shape than most others. The number of people who have died or been seriously ill as a consequence of the virus, whilst tragically high, is nonetheless comparable with most of the best-performing countries.

That said, we believe there is a critical need for strategic statistical oversight of the whole process of anticipating, managing and learning from the current pandemic to improve the quantitative information and advice provided to policy makers. This proposal outlines quantitative aspects of a plan to enable Australia to deal more efficiently and effectively with future such events, thus enhancing both the social and the economic welfare of its people. Indeed, expeditious action may well assist materially in managing possible future waves of the current pandemic, and its aftermath.

A dispassionate assessment of Australia’s health and economic response to the pandemic over the last six months reveals some very significant inadequacies in the data, statistical analysis and interpretation used to guide Australia’s preparations and actions. Data to answer some of the most basic of questions about prevalence in population have not been available and remain unavailable. For example, one key shortcoming has been the lack of data to obtain an early understanding of the extent of asymptomatic and mildly symptomatic cases or the differences across age groups, occupations or ethnic groups and their reproduction characteristics. Another is the lack of early-warning indicators of the progress of the virus. In our view, this has meant that that Australian Governments and the National COVID-19 Coordination Commission have been significantly impaired in their ability to carry out their duties, and has resulted in undue reliance on ‘one size fits all’ interventions with a larger economic impact than may have been necessary.

For the strategy of finding and isolating positive cases to be effective, early detection is essential. Waste-water surveillance provides a 5-day advance warning of the presence of the virus, and is already in use in a number of countries, yet is receiving little attention from Australian health authorities.

Minimising the impact of a novel virus depends critically on ongoing acquisition, integration, analysis, interpretation and presentation of a variety of data streams to inform the development, execution and monitoring of appropriate strategies. Figure 1 [see Full PDF] captures the essential components of such an approach: four basic phases, from initial detection to post-pandemic, and critical steps in each stage to plan for acquisition of diverse data and to act expeditiously on the information they contain. A broad range of statistical skills, knowledge and know-how is needed to support most aspects of control, with sound independent statistical guidance being provided at the very highest levels of decision-making.

Pandemics such as the current episode are times of crisis. And in a time of crisis, it is essential that people and groups who are generally in (healthy) competition need to work collaboratively and collegially. Data, information, models and approaches need to be shared rapidly for learning, exploitation, critical peer assessment and comparison, so that the Government can be confident that it is receiving the best possible advice available as soon as possible.

It is our strong recommendation that a multi-disciplinary Task Force be established expeditiously to develop a Pandemic Information Plan. As well as those involved from the policy side, the membership should include a statistician with expertise in statistical modelling and analysis, an official statistician, an epidemiologist, a medical researcher, economist and public health official. Timely action by the Task Force may even prove to be of significant assistance in managing future waves of the current pandemic.

[Full PDF]

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