Chapter 2 Data Collection and Sampling

Always remember the ultimate goal of inferential statistics: We want to say something important about the characteristics (parameters) of a population without ever observing the entire population. Therefore, the best thing we can do is to draw a sample (i.e., subset) from the population and use it to calculate characteristics (statistics) of a sample and draw inference on the population parameters.

The reason why we can say something about a population parameter of interest solely by looking at the statistics from a sample is because we are under the assumption that the sample has the same characteristics of the population. In other words, we say that the sample average is a good guess for the population average, the sample standard deviation is a good guess for the population standard deviation, etc. This is not an assumption that is simply made by wishful thinking. In fact, there is an entire field of statistics devoted to proper sample selection. We won’t spend a lot of time on this very important matter, but we will discuss a few sampling methods so you can rest assured that our crucial assumption of similar sample and population characteristics has a reasonable chance of holding.

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