Back in Chapter 9, the Central Limit Theorem showed us that samples are typically representative of the populations from which their drawn. It follows then that the mean of a randomly selected sample should provide a fair estimate of the corresponding population mean.
Don't confuse the p-value with the population proportion (p). The p-value is an alternate to the rejection region method in deciding whether to reject the null hypothesis. A larger P value indicates the null is the "better explanation" for your data (and that the pattern you're seeing is by chance), while a smaller p-value indicates that your data rejects the null hypothesis and your data reflects the larger population.
You'll need to organize your study time to get a good mark on the exam. Here are my suggested minimum amounts of time to study each chapter - as well as the contents you should be focusing on.
One Way Analysis of Variance is easier than it looks, but there are A LOT of calculations to perform! If you start by summarizing each sample into a few statistics, the process is much easier to handle.
Your textbook can be confusing when it tries to explain the Laws of Expected Value. It can be easier to understand what's happening if you think about them as the Laws of THE NEW Expected Value. I explain in more detail in this video.
Learn the difference between SAMPLING ERROR and NON-SAMPLING ERROR, and how they are introduced to the samples that researchers collect.
Test your understanding of multiple linear regression - each question is accompanied by a mini-video lecture showing you how I decided which solution was the correct one.
A few years ago Type II error calculations were removed from the course due to their level of difficulty. Now they're back! Make sure you understand their solutions because they are VERY likely to be on the upcoming final.
Another simple keyword changes this question once again from what looks like a Chapter 8 normal distribution question into a Chapter 7 binomial question. Watch and find out how
Using the sample mean to estimate the population mean may seem obvious, but a good estimator for a population parameter must have certain characteristics to be dependable. In this video I discuss the qualities of a good estimator: unbiasedness, consistency, and relative efficiency.