The Simulation Design Cycle: A step-by-step roadmap for creating a valid model. Students learn to define the Problem, design the Tool, conduct the Trials, and analyze the Results to reach a conclusion.
Defining the Components: Teaches students how to identify the "trial" and the "successful outcome." For example, if simulating a cereal box prize collection, students learn to define what constitutes a "complete set."
Selecting Simulation Tools: Hands-on practice using various probability tools, including Random Number Tables, Graphics Calculators (RanInt function), Dice, and Spinners. Students learn which tool best fits the theoretical probabilities of the situation.
Allocation of Digits: A core skill for simulation. Students master how to assign numbers to outcomes (e.g., "0-2 represents a win, 3-9 represents a loss") to accurately reflect given percentages or fractions.
Conducting and Recording Trials: Provides structured templates for recording data. Students learn the importance of conducting a sufficient number of trials (usually at least 30) to ensure the experimental probability is a reliable estimate.
Calculating Experimental Probability: Practice in converting raw results into statistical findings. Students learn to calculate the mean, median, and proportions from their simulated data to answer their original research question.
Visualising Results: Instructions on how to create Dot Plots and Histograms to show the distribution of their simulation results, helping to identify patterns and the "most likely" outcomes.
Evaluating the Model: A critical requirement for Merit and Excellence. Students practice identifying the Assumptions made during the simulation (e.g., "assuming every box has an equal chance of containing a prize") and discussing how these might differ from real-world conditions.
Dealing with Variability: Explores why two students using the same model might get different results. This section discusses the impact of sample size on the precision of the estimate.
Achievement, Merit, and Excellence Scaffolding: Tasks lead students from performing a simple simulation to the "Excellence" level of critiquing the simulation’s limitations and suggesting improvements for a more realistic model.
NCEA-Style Practice Tasks: Includes full practice assessments that mimic the format of actual internal standards, helping students build the stamina and clarity required for formal assessment.
Glossary of Simulation Terms: A guide to essential vocabulary—such as Trial, Outcome, Tool, Allocation, and Theoretical vs. Experimental Probability—ensuring students use the correct technical language in their reports.