Inference for Categorical Data: Proportions Overview — AP Statistics
1. Unit at a Glance
This unit builds on your foundational knowledge of sampling distributions and the general framework for statistical inference to focus specifically on categorical data measured as proportions. You will start with core concepts for single proportion inference, then extend those frameworks to compare proportions between two independent groups.
The learning sequence separates confidence interval and hypothesis test skills, starting with single proportion concepts before moving to two-proportion comparisons, to let you build mastery incrementally aligned with AP Exam expectations.
Common Pitfalls
Why: Pooling is only appropriate for hypothesis tests testing equal proportions, not confidence intervals
Why: Inference for proportions relies on an approximately normal sampling distribution; failing this invalidates results
Why: Hypothesis tests assume the null hypothesis is true, so we use the null proportion for SE calculation