Idea behind hypothesis testingExamples of null and alternative hypothesesP-values and significance testsComparing P-values to different significance levelsEstimating a P-value from a simulationUsing P-values to make conclusions
Introduction to Type I and Type II errorsExamples identifying Type I and Type II errorsIntroduction to power in significance testsExamples thinking about power in significance testsConsequences of errors and significance
Constructing hypotheses for a significance test about a proportionConditions for a z test about a proportionReference: Conditions for inference on a proportionCalculating a z statistic in a test about a proportionCalculating a P-value given a z statisticMaking conclusions in a test about a proportionSignificance test for a proportion free response exampleSignificance test for a proportion free response (part 2 with correction)
AP Stats: DAT (BI), DAT‑3 (EU), DAT‑3.F (LO), DAT‑3.F.1 (EK), DAT‑3.F.2 (EK)
Writing hypotheses for a significance test about a meanConditions for a t test about a meanReference: Conditions for inference on a meanWhen to use z or t statistics in significance testsExample calculating t statistic for a test about a meanUsing TI calculator for P-value from t statisticUsing a table to estimate P-value from t statisticComparing P-value from t statistic to significance levelFree response example: Significance test for a mean
If a basketball player says they make 75% of the shots they take, but they only make 65% of shots in a sample, does that mean they're lying? Significance tests give us a formal process for using sample data to evaluate how plausible a claim about a population value is. We calculate P-values to see how likely sample results are to occur by random chance, and we use P-values to make conclusions about hypotheses.
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