Interpreting Data to Predict and Conclude progression

The ability to interpret data is an important part of using and understanding statistics in everyday life.

Find more information about the development of the ability to reason statistically.

Most adults will be able to:			Activities
1.		There are gaps at the first steps of this progression because learners need to be able to sort, organise and describe data before they can interpret data. They need to be able to prepare and analyse data in order to interpret it.
3.	make sensible statements based on the general features of a data set.	Learners can: talk about the shape of a data display in terms of symmetry, clusters and spread use terms such as most common, least common, unusual gaps or unusual peaks to describe features of the data set or display discuss or explain any differences between what their data set/display shows and what they had expected to find or see explain how data can be used to provide greater certainty and more accurate predictions.	Sort, represent and interpret category data Learners sort and organise category data and represent it on graphs.
4.	draw conclusions and make predictions, based on evidence from the data.	Learners can: make summary statements about data, referring to features such as clusters, range, median and subsets within the larger data set discuss trends in the data and make evidence-based predictions discuss possible reasons for variation between one data set and other similar sets of data discuss data-based summaries and supporting graphic displays in terms of certainty make predictions that extend beyond basic data interpretation to include elements of uncertainty, acknowledging the role of variation.	Sort, represent and interpret number data Learners sort and organise number data and represent it on graphs.
6.	use observations based on samples to make conjectures about the populations from which the samples were taken.	Learners can: discuss the features of the normal curve and explain how it models the spread of a typical random variable (for example, height or weight) use two or more sample data sets to make generalisations about a larger population, estimating, for example, the mean of a particular variable, its spread or the likelihood of a particular event occurring compare data sets and displays as a means of learning to determine the reasonableness of their own generalisations identify and discuss sources of potential bias and distortion (in sampling methods and in statistical summaries and graphic displays) acknowledge sources of uncertainty, based on an awareness of variability.	Understanding media reports that include statistical information Learners develop the ability to interpret and critically analyse a media report that includes statistical information.