Find below the Learning Outcomes, mathematical concepts and progression continua associated with the strand units of "Data" and "Chance". This page will be updated with further support materials and video examples of children's learning in the coming weeks and months.
explore, interpret and explain data in a variety of ways for a range of purposes.
pose questions of interest, record and use data as evidence to answer those questions and communicate the findings.
pose questions of interest and collect, display and critically analyse data in arange of ways for arange of purposes and communicate the findings.
pose questions, collect, compare, summarise and represent data selectively to answer those questions.
critically analyse and evaluate findings; and communicate inferences, conclusions and implications from the findings.
Data is all around us and helps us interpret the world.
A data set is a collection of information that can provide answers to questions we ask.
Data can be collected and represented in many ways.
Data displays (e.g., tables, picture graphs, block graphs) are a useful way of conveying information.
Objects and sets can be sorted according to one or more attributes.
Investigations are cyclical and are motivated by posing a question.
Data investigations involve a process of collecting, representing and analysing data, and communicating conclusions that answer questions.
Data can be qualitative (it describes something) or quantitative (it holds numerical value).
Different types of data require different graphs and different statistical measures.
Graphs are tools which communicate distribution, shape, centre and variability of data.
Data displays can hold a vast volume of information which can be reasoned about and from which decisions and inferences can be made.
Data displays are selected and justified based on their ability to communicate aspects of the data and answer the questions posed. Moving between data displays allows for further comparison and analysis.
Measures of centre (e.g. mean as the fair share, and median as the middle-ordered value of the data) are one-number summaries of entire distributions.
The range is a measure used to capture variability or spread of the data.
Secondary data can be analysed to make observations or inferences and to draw logical conclusions.
Informal inference is about moving beyond the data collected (sample) to a wider context (population).
Data can be distributed in different ways. Such distributions of data can be compared according to their shape.
The mean, median and/ or mode are measures of centre which communicate different middles of the data and provide a range of insights.
Samples can be drawn from a population of data as representative evidence, to make generalisations and determine the degree of confidence or certainty about the generalisation.
Reported data can be evaluated in terms of its representativeness, intentionality and reliability.
Data displays (e.g. graphs) can be used to represent the variability in the data, the measures of centre and to compare between two groups.
Click on the image to access the progression continuum for the strand unit of 'Data'
describe and test predictability and (un)certainty in events.
use probability to make informed decisions and predictions.
represent and express probability in different forms.
Events in everyday life involve chance. Some events are more likely to happen than others.
If an event is unlikely to happen, it has a low probability. If something is likely to happen, it has a high probability.
Expected or predicted outcomes of an event can differ from actual outcomes.
Investigating chance allows decision-making and predictions about everyday events and occurrences.
Probability can be represented on a scale between 0 – 1.
The experimental probability of an event occurring may not always match the theoretical probability.
The probability that a specific outcome will occur can be represented as a fraction, decimal or percentage.
A sample space contains all possible outcomes of an experiment.
Probability can be described in proportional terms and is calculated by dividing the number of ways the identified outcome can happen over the total number of possible outcomes.
As you repeat a trial independently a large number of times, the average result becomes increasingly closer to the expected value.
Click on the image to access the progression continuum for the strand unit of 'Chance'