Doing inferential statistics mean generalizing to a population our observations from a sample. We're able to do this by making assumptions about sampling distributions.
What are sampling distributions? Read David Lane's Introduction to Sampling Distributions module.
Take the tutorials on central limit theorem and sampling distribution of the mean by WISE (Web Interface for Statistics Education).
Try this interactive "experiment" on central limit theorem.
The two major types of inferential statistics are parameter estimation and hypothesis testing.
Parameter estimates can be either point estimates or confidence intervals (CI). Read the difference between the two in Stat Trek's tutorial on estimation.
Because point estimates are almost always wrong, CIs give a more meaningful picture of parameters.
Let's devote a separate subsection for hypothesis testing because that's what we will be doing for most of the semester. Click here to go to "How are hypotheses tested?" section.
Below is a ppt presentation from the publishers of our book:
Click HERE if you wish to view the downloadable handout with notes.
Review the lesson using the the following materials from Wadsworth Cengage:
Take Self Test 10 now.
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