Justifying Data Features

Name:

Date:

🎯 WALT (We Are Learning To)

We are learning to: Identify and justify key features in data visualisations using appropriate statistical language and context.

Success Criteria:

I can identify mean, median, range, IQR, outliers, and distribution shapes in data visualisations

I can justify why these features appear by connecting them to real-world context

I can use multiple visualisations to support my explanations

I can communicate findings using correct statistical language

📊 Part 1: Identifying Data Features

1. A box plot shows Auckland rainfall data (mm) over 12 months. The median is 85mm, Q1 is 60mm, Q3 is 110mm, with one outlier at 180mm in March. Which statement best justifies the outlier?

March had measurement errors

March experienced unusual weather patterns like cyclones

The data collection was faulty

March always has high rainfall

2. A histogram of Year 11 student heights shows a right-skewed distribution. What does this suggest about the data?

Most students are very tall

Most students are of average height with few very tall students

The data is normally distributed

There are equal numbers of short and tall students

3. Which features should you identify when analysing data visualisations? (Select all that apply)

Central tendency (mean, median)

Spread (range, IQR)

Distribution shape

Outliers

Colour of the graph

✏️ Part 2: Justifying Features in Context

4. A scatter plot shows the relationship between study hours and test scores for your class. There's a positive correlation with one outlier - a student who studied 8 hours but scored only 45%. Justify why this outlier might exist.

Sentence starter (if needed): This outlier appears because _______________

5. House prices in Wellington show a mean of $850,000 but a median of $720,000. The distribution is right-skewed. Justify why the mean is higher than the median in this context.

Sentence starter (if needed): The mean is higher than the median because _______________

6. A box plot of daily temperatures in Christchurch shows Q1 = 8°C, median = 12°C, Q3 = 18°C, with several outliers above 25°C. Using statistical language, justify what these outliers represent.

7. Extension Activity: You're analysing two different visualisations of the same traffic accident data - a bar chart and a line graph. The bar chart shows clear seasonal patterns, but the line graph makes the data look more dramatic. Critically evaluate how the choice of visualisation might affect interpretation and justify which is more appropriate.

8. Challenge: Create your own justification scenario. Describe a real New Zealand context where you might see an outlier in data, then explain what statistical features you would look for and how you would justify them to someone who doesn't understand statistics.