Chapter 2. Personality

In this applet, you’ll expand on your knowledge of research design, specifically longitudinal studies, and the concept of standard error (defined in the first Statistical Lesson) and the impact of the size of the sample.

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Statistical Lesson: Our goal with most statistics is to make guesses about the population using what we learn from a sample. When we calculate the sample mean or average, we can also determine how close this average might be to the actual population average. We estimate this via the standard error, which is how much our sample average deviates from the population average. If the standard error is small, then we are more confident that the sample average is closer to the population average. However, if the standard error is large, then we are less confident that our sample can accurately predict the population average. Since our goal is typically to make assumptions about the population using the sample, then it is best to reduce the size of our standard error.

The sample size is important because the larger the sample, the better it reflects the population. Smaller data sets can have high levels of variability and may be misleading. Fortunately, we don’t necessarily need to sample everyone or everything in the population. Moderately sized data sets are typically sufficient and more realistic to achieve.

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Having larger sample sizes decreases the variability and standard error, allowing us to better predict the population values. However, which of the following is a reason why we do NOT include the entire population in a sample? Select all that apply.

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