Activity 01
Data Spread Challenge
Provide class height data. Students calculate range and quartiles, then draw box plots. Compare plots from two sets to discuss spread differences.
Explain how the range provides a basic understanding of data variability.
Facilitation TipDuring Data Spread Challenge, ask students to predict the range before calculating it to highlight how outliers inflate this measure.
What to look forProvide students with a small dataset of 10-12 numbers (e.g., daily temperatures for a week). Ask them to calculate and write down the range, Q1, Q2, and Q3. Check their calculations for accuracy.
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Activity 02
Quartile Sorting Game
Give jumbled data cards. Pairs arrange, find quartiles, and verify with formula. Share findings with class.
Evaluate the utility of quartiles in dividing a dataset into four equal parts.
Facilitation TipIn Quartile Sorting Game, have students physically arrange number cards into quartile groups to reinforce the idea of 25%, 50%, and 75% splits.
What to look forGive each student a set of data. Ask them to calculate the range and the interquartile range (IQR). On the back, they should write one sentence explaining what the IQR tells us about this specific dataset.
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Activity 03
Real-Life Dataset Analysis
Collect daily temperatures. Individually compute range and quartiles, plot box whisker. Discuss weather variability.
Construct a box-and-whisker plot from a given set of data.
Facilitation TipFor Real-Life Dataset Analysis, provide datasets from student-relevant contexts like exam scores or pocket money to increase engagement.
What to look forPresent two different box-and-whisker plots side-by-side (e.g., test scores for two different classes). Ask students: 'Which class shows more consistency in scores? How do the box plots help you answer this?'
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Activity 04
Group Comparison Plot
Small groups get scores from different exams. Calculate measures, create comparative box plots on chart paper.
Explain how the range provides a basic understanding of data variability.
Facilitation TipWhen doing Group Comparison Plot, ensure groups present their box plots on the same axis to facilitate direct visual comparison.
What to look forProvide students with a small dataset of 10-12 numbers (e.g., daily temperatures for a week). Ask them to calculate and write down the range, Q1, Q2, and Q3. Check their calculations for accuracy.
RememberUnderstandApplyAnalyzeSelf-ManagementRelationship Skills
Generate Complete Lesson→A few notes on teaching this unit
Teachers should introduce range first because it is intuitive, then show its limitations with an example like {1, 2, 3, 4, 100}. Quartiles should be taught as positions, not averages, using the median as a bridge. Research suggests using humanities datasets (e.g., literature word counts) before science data to reduce abstraction. Avoid teaching box plots too early; let students internalise quartiles through sorting before formalising with plots.
By the end of these activities, students should confidently calculate range and quartiles, explain their differences, and use box plots to compare datasets. They will also justify when to use range versus quartiles or IQR based on dataset characteristics.
Watch Out for These Misconceptions
During Data Spread Challenge, watch for students who assume a larger range always means more spread in the data.
After they calculate, ask them to identify if the large range is due to an outlier and recalculate the IQR to see the actual spread of central data.
During Quartile Sorting Game, watch for students who treat quartiles as averages of halves.
Have them physically count the cards to the 25%, 50%, and 75% marks to reinforce that these are position values, not calculations.
During Group Comparison Plot, watch for students who include outliers in the whiskers of box plots.
Remind them to check values beyond 1.5 times the IQR from Q1/Q3 and mark these as outliers beyond the whiskers on their plots.
Methods used in this brief