2026 7 Maths Unit 4.2 Rubric

Actual Rubrics are based on teacher-elaborations of selected content descriptors for the assessment that sufficiently map to all of the Achievement Standard components.

Achievement Standard Rationaliser for Assessment Construction (Non-Assessment Specific)

Achievement Standard Component	E	D	C	B	A
They plan and conduct statistical investigations involving discrete and continuous numerical data, using appropriate displays.	Did not plan an investigation or display data.	Requires heavy support to collect data and often chooses inappropriate visual displays or those without annotation	They plan and conduct statistical investigations involving discrete and continuous numerical data, using appropriate displays.	Independently designs robust investigations, flawlessly categorising data and creating highly effective displays.	Executes complex statistical investigations on real-world issues, innovating data displays for maximum communicative impact.
Students interpret data in terms of the shape of distribution and summary statistics, identifying possible outliers.	Cannot interpret data shape or identify outliers.	Reads basic data but misses outliers or misinterprets the shape of the distribution.	Students interpret data in terms of the shape of distribution and summary statistics, identifying possible outliers.	Accurately analyses distribution shapes (e.g., skewed, symmetrical) and correctly identifies and contextualizes all outliers.	Critically evaluates the impact of distribution shapes and extreme outliers on the overall narrative of a dataset.
They decide which measure of central tendency is most suitable and explain their reasoning.	Fails to calculate mean, median, or mode.	Calculates central tendency but cannot justify which measure is best for the data.	They decide which measure of central tendency is most suitable and explain their reasoning.	Insightfully selects the best measure of central tendency based on data spread and skew, clearly justifying the choice.	Deeply analyses how changing the measure of central tendency could manipulate the interpretation of the dataset.

Content Descriptor Rubric for Content Rationalisation (Non-task specific)

E	D	C 😊	B	A
Asks biased survey questions resulting in unusable data.	Collects valid data but struggles to categorize it as discrete or continuous.	AC9M7ST03 plan and conduct statistical investigations involving data for discrete and continuous numerical variables; analyse and interpret distributions of data and report findings in terms of shape and summary statistics	Identifies lurking variables and accounts for them when structuring the investigation methodology.	Synthesizes findings into a compelling statistical report that strictly delineates correlation from causation.
Creates chaotic graphs with missing axes or inconsistent scales.	Builds standard bar charts but avoids complex displays like stem-and-leaf plots.	AC9M7ST02 create different types of numerical data displays including stem-and-leaf plots using software where appropriate; describe and compare the distribution of data, commenting on the shape, centre and spread including outliers and determining the range, median, mean and mode	Compares two distinct data sets simultaneously using back-to-back stem-and-leaf plots to draw comparative insights.	Innovates the visual presentation of data to highlight subtle distribution anomalies that standard charts obscure.
Cannot calculate mean, median, mode, or range.	Calculates central tendency but cannot justify which specific measure provides the most useful insight.	AC9M7ST01 acquire data sets for discrete and continuous numerical variables and calculate the range, median, mean and mode; make and justify decisions about which measures of central tendency provide useful insights into the nature of the distribution of data	Clearly and logically justifies the choice of central tendency based strictly on distribution shape and the presence of outliers.	Critically evaluates how utilizing different measures of central tendency can manipulate the interpretation of the exact same data set.