Simple hypothesis testingIdea 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 errorsType 1 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 proportion
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
Hypothesis testing and p-valuesOne-tailed and two-tailed testsZ-statistics vs. T-statisticsSmall sample hypothesis testLarge sample proportion hypothesis testing
About this unit
Significance tests give us a formal process for using sample data to evaluate the likelihood of some claim about a population value. We calculate p-values to see how likely a sample result is to occur by random chance, and we use p-values to make conclusions about hypotheses.