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
Students list sample spaces for single step experiments, assign probabilities to outcomes and predict relative frequencies for related events.
Does not list outcomes or assign probabilities.
Lists simple sample spaces but makes errors when predicting relative frequencies.
Students list sample spaces for single step experiments, assign probabilities to outcomes and predict relative frequencies for related events.
Accurately defines comprehensive sample spaces and precisely predicts frequencies for multiple related events.
Connects theoretical probabilities and sample spaces to analyse complex single-step experiments.
They conduct repeated single-step chance experiments and run simulations using digital tools, giving reasons for differences between predicted and observed results.
Does not run experiments or compare results.
Runs simulations but struggles to explain why observed results differ from predicted ones.
They conduct repeated single-step chance experiments and run simulations using digital tools, giving reasons for differences between predicted and observed results.
Proficiently uses digital tools to run extensive simulations, offering clear, statistically sound reasons for variances.
Deeply analyses variations in large-data digital simulations, critiquing experimental design and probability theory.
Content Descriptor Rubric for Content Rationalisation (Non-task specific)
E
D
C 😊
B
A
Guesses outcomes based on personal bias
Lists basic outcomes but misses combinations in slightly more complex single-stage events.
AC9M7P01 identify the sample space for single-stage events; assign probabilities to the outcomes of these events and predict relative frequencies for related events
Constructs exhaustive, highly organized sample spaces (e.g., two-way tables) to ensure zero omissions.
Evaluates the fairness of a game by mathematically proving the exact probability of every single outcome.
Runs the digital simulation but lacks the statistical vocabulary to explain variations.
AC9M7P02 conduct repeated chance experiments and run simulations with a large number of trials using digital tools; compare predictions about outcomes with observed results, explaining the differences
Leverages the Law of Large Numbers to demonstrate how observed frequencies converge on theoretical probability: Can include a short explanation with example calculation from simulation
Manipulates simulation parameters to expose statistical flaws in experimental design or biased random number generation.