

This IB Math AI SL 4.11 – Hypothesis Testing & Significance resource develops students’ understanding of how statistical evidence is used to make formal decisions about claims. Students build fluency with null and alternative hypotheses, significance levels, p-values, and the decision rule for rejecting or failing to reject (H_0). Core ideas such as one-tailed vs two-tailed tests, interpretation of p-values, and Type I and Type II errors are embedded early, giving students a clear conceptual framework before calculations .
Structured tasks guide learners through the major SL testing procedures: chi-squared tests for independence, chi-squared goodness of fit, and the pooled two-sample t-test. Students practise setting up hypotheses, calculating degrees of freedom, finding test statistics and p-values using technology, and writing conclusions in context. Realistic scenarios—surveys, preferences, product testing, and comparative samples—help connect abstract testing procedures to decision-making situations. Extended exam-style problems develop multi-step reasoning, combining hypothesis setup, calculation, interpretation, and evaluation of assumptions. With scaffolded practice, mixed review, and a full answer key, this resource supports SL classroom instruction and independent study in line with IB Mathematics AI expectations.
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