Data Variation and Outliers
This pack includes six differentiated worksheets designed to help students understand data variation and outliers. Students analyse real-world data sets, identify outliers and explain their potential causes. Each worksheet includes step-by-step questions to guide students through identifying patterns, understanding variations and discussing the implications of their findings. Suitable for a variety of ability levels, this resource is ideal for fostering critical thinking and mathematical reasoning in the classroom.
Learning goals
- We are learning to interpret and compare the four types of data sets (ordinal, nominal, discrete, continuous) using comparative displays and determining the range, mode (frequency) and shape of data in graphs, dot plots and bar charts.
- We are learning to interpret secondary data (data collected by others) represented in digital media, books, scientific papers etc. and identify potentially misleading data representations.
- We are learning to pose questions, collect and interpret categorical or numerical data by observation or survey.
- We are learning to interpret and compare data displays, including side-by-side column graphs, tables and diagrams for two categories (e.g. age, height) and compare the usefulness of each display for data interpretation.
- We are learning to interpret secondary data (data collected by others) represented in digital media, books, scientific papers etc. and identify potentially misleading data representations.
Curriculum alignment
Australian Curriculum covers QLD / SA / WA / NT / TAS / ACT.
Differentiation
Modifications
• Provide guiding hints or examples for students needing additional support.
• Allow students to work in pairs to foster collaboration and shared understanding.
• Offer simpler questions for students focusing on basic identification rather than detailed analysis.
Extensions
• Ask students to create their own data sets and include intentional outliers for peers to identify and analyse.
• Challenge advanced learners to calculate the effect of removing an outlier on the mean and median of the data.