Covered in AI SL and AI HL, hypothesis testing introduces the formal framework for making statistical inferences. Students formulate null and alternative hypotheses, interpret p-values against significance levels, and apply the χ² test for independence in contingency tables, the χ² goodness of fit test, and the t-test for comparing means of two populations. AI HL extends this substantially to include critical regions, tests for population mean using normal and t-distributions, tests for proportion using the binomial distribution, tests using the Poisson distribution, hypothesis testing for the correlation coefficient ρ, and Type I and Type II errors with associated probability calculations.
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Video lessons
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