Hypothesis Testing for One Proportion
The previous section on the Central Limit Theorem reinforced the idea that different samples from the same population will give different values of a particular "statistic" (a proportion in this case) but that the sample values will predictably cluster around the true population value; in other words, a sample statistic usually won't stray too far from the true population parameter.
If we find a sample proportion that is really far away from the assumed population proportion, we'll be inclined to think the evidence from the sample is suggesting the assumed parameter isn't correct.
What, then, does "far away" mean? That's where the math behind the Central Limit Theorem comes in! It allows us to measure the probability of just such a situation.
Get ready: Print out the blank notes: 8.1 - 8.3: Hypothesis Testing for a Proportion
Open StatCrunch and sign in: www.statcrunch.com
Next: Watch the video on Section 8.1-8.3: Video - sorry that took so long...troubles uploading to YouTube, but I can upload directly (I just found out), so...yay!
Here are the filled-in notes: 8.1 - 8.3 Notes Filled In
Finally: Do the assigned homework problems for 8.1 - 8.3
New! Additional Hypothesis Test Example
Filled-in Notes (better than from video!)