ABSTRACT
At various points in a statistics course, students sometimes lose sight of the “big picture”—how did we get to where we are now? Let’s take a look at the test statistics and interval estimates we have covered so far and how they have led us to the current topic:
• The z test statistic allowed us to compare one sample mean to one population mean using a hypothesized numeric value for μ from the null hypothesis. A critical value from the standard normal distribution was used to test the null hypothesis and compute an interval estimate of the population
Introduction Going Beyond the
Independent-Samples t Test
Variance Between Groups and Within Groups
One-Way ANOVA F Test: Logic and Hypotheses
Computing the One-Way ANOVA F Test
Critical Values and Decision Rules
Numeric Example of a One-Way ANOVA F Test
Testing the Null Hypothesis Assumptions and
Robustness How to Tell Which Group Is
Best Multiple Comparison
Procedures and Hypotheses
Many Statistics Possible for Multiple Comparisons
Confidence Intervals in a One-Way ANOVA Design
What’s Next
mean. The z test statistic and its corresponding confidence interval required a hypothesized value for one parameter, μ, as well as knowledge of another parameter, the population standard deviation (or variance).
