LESSON: Understanding Confidence Intervals
Basic Overview of Confidence Intervals
In this lesson book we'll be learning about confidence intervals. Before we really dive into the topic though, watch this video so you have a basic idea of what confidence intervals are for, what they calculate and why they are important.
It might be helpful to watch this video more than once so you can really understand what she is explaining.
Here are the main points to understand:
1. We calculate the mean of a large population by taking a sample from that population first. If we select our sample randomly and give every member of the population an equal probability of being selected, then we can say that the mean of the sample is a good estimate of the mean of the population. However, if we select different sample groups from the same population, we will ALWAYS get different sample means. This is called sampling error.
2. When reporting the measures of a population (like mean, median, and range) it's good practice to give a range of possible values, instead of an exact value. For example, saying, "The estimated mean of the population is 140 grams." makes it sound like we are exactly sure of that number. But that's impossible due to sampling error. Rather, it's better to say, "The estimated mean of the population is between 137-143 grams."
3. A confidence interval communicates how accurate our estimates are likely to be.
4. The width of a confidence interval (or how accurate or estimate is) is determined by the following two things:
a) Variation: Are the members of the population similar? Or do they vary largely? Small variation means our confidence interval is larger. We are more sure our estimate is accurate.
b) Sample Size: If our sample size is large, then sampling error is reduced and we can have a larger confidence interval.