Clarification details
Updated July 2019. The name of the first section has been changed to ‘Question, Plan and Conclusion’.
The assessment activity needs to include sufficient contextual knowledge to provide opportunities for statistical insight.
Question, Plan and Conclusion
Students can be provided with the experimental situation they will investigate. The experiment needs to involve an intervention, and the investigative question needs to be about the effect of that intervention. A suitable investigative question would be: ‘Does changing from jumping off your dominant foot to jumping off your non-dominant foot affect how far you can jump horizontally?’
Part of the plan for the experiment will include students making their own decisions about the intervention, the variables that they will measure and how they will measure them.
At all levels of achievement, the conclusion needs to include a suggestive inference. For example, a student might conclude jumping off the dominant foot appears to have caused an increase in the distance jumped for the experimental group.
Comparison of two independent groups
The experimental units need to be randomly assigned to the two groups (the control group and the treatment group).
Students should produce dot plots and/or box plots of each independent group. Students need to compare and discuss features such as shape, shift, centre, spread and unusual features and link these discussions to the treatment.
Paired comparison
In a paired comparison, ‘before’ and ‘after’ measurements are taken on the same person or object.
The displays should retain the link between the observations and the experimental unit. Appropriate displays could be an arrow plot or a dot plot and/or box plot of the differences. Students need to compare and discuss the features of the displays and how the effect of the treatment varies from one experimental unit to another.
Required quality of student response
For Achieved, it is sufficient to provide evidence of using each component of the investigation process.
Merit is with justification. This involves students linking components of the process to the context. In their plan students need to consider related variables and their possible effects on the investigation. Findings need to be supported with evidence gained from the experiment.
Excellence is with statistical insight, and involves integrating statistical and contextual knowledge, considering related variables, their possible effects and plans to ensure that the data collected is consistent. For example, a student might use contextual knowledge to help explain what is observed, for example longer jump distances when jumping off the dominant foot.