
IB Math AI HL 4.12 – Designing Reliable Surveys and Tests
This lesson focuses on how to design valid and reliable surveys for data collection in mathematical and real-world contexts. Students learn to select relevant variables that directly address a research question and to eliminate unnecessary ones that add noise or bias.
The slide deck highlights the importance of question phrasing, emphasizing neutrality, structure, and consistency in answer options to avoid leading or ambiguous questions. Key statistical principles, such as reliability and validity, are introduced in detail. Reliability refers to the consistency of results across repeated measurements, while validity measures whether the survey actually assesses what it claims to. Students explore methods of testing reliability, including test–retest and parallel forms, and methods of testing validity, such as content validity (expert review) and criterion-related validity (correlating results with an external benchmark).
Realistic examples and case studies help illustrate how to ensure unbiased data collection, appropriate variable selection, and structured question formats. The topic also connects to chi-squared testing, discussing categorization of data and the choice of degrees of freedom when estimating parameters from grouped data.
By the end of the lesson, students can confidently design, test, and refine surveys and questionnaires that produce statistically meaningful and interpretable results. Fully aligned with IB Math AI HL Topic 4.12 – Designing Reliable Surveys and Tests, this resource builds essential analytical and critical-thinking skills for applied statistics and research-based modeling.
Something went wrong, please try again later.
This resource hasn't been reviewed yet
To ensure quality for our reviews, only customers who have purchased this resource can review it
Report this resourceto let us know if it violates our terms and conditions.
Our customer service team will review your report and will be in touch.