📊 Part 1: Reading Data Visualisations
Context: A New Zealand high school surveyed 100 Year 11 students about their weekly study hours and test scores in mathematics.
1. Look at this scatter plot description: "The scatter plot shows study hours (0-20) on the x-axis and test scores (0-100) on the y-axis. Most points cluster between 5-15 study hours with scores 60-90. There are 3 outliers: one student studied 2 hours and scored 85, and two students studied 18+ hours but scored below 50."
What can you infer about the relationship between study hours and test scores?
More study hours always leads to higher scores
There's generally a positive relationship, but other factors matter
Study hours don't affect test scores at all
The relationship is perfectly linear
2. Based on the scatter plot description above, if you drew a line of best fit, what test score would you predict for a student who studies 10 hours per week?
Predicted score: _________ (Give a range if needed)
3. The three outliers mentioned could suggest:
Some students are naturally gifted
Study quality matters more than quantity
There might be measurement errors in the data
Other factors like sleep or stress affect performance
🔍 Part 2: Critical Evaluation
4. A news headline reads: "Study Shows 90% Increase in Student Achievement!" The bar graph shows test scores rising from 50 to 95, but the y-axis starts at 45 instead of 0.
What makes this visualisation potentially misleading?
5. You're investigating whether there's a difference in mathematics performance between students from different regions in New Zealand. What type of data visualisation would be most appropriate?
Scatter plot
Box plots comparing regions
Line graph
Pie chart
6. When interpreting data about educational achievement across different ethnic groups in New Zealand, what should you consider?
Socio-economic factors
Access to educational resources
Cultural differences in assessment methods
Sample size and representation
💭 Part 3: Communication and Reflection
7. A box plot shows that the median mathematics score for Auckland students is 75, while the median for Canterbury students is 70. The Auckland data has a larger interquartile range (IQR = 25) compared to Canterbury (IQR = 15).
Explain what this tells us about mathematics performance in these two regions:
8. You notice that your data about student performance doesn't include information about students' first language, family income, or school resources. How might this missing information affect your conclusions?
9. Extension Challenge: Design an investigation question about student wellbeing that would require creating multiple data visualisations. What variables would you measure and what types of graphs would you use?
