PR 680 Lecture Notes - Lecture 55: Confidence Interval, Statistical Parameter, Sample Size Determination

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In order to understand confidence intervals, we need to understand sampling and sampling error. To find things that about a population of interest, it is common practice to take a sample. A sample is a selection of objects or observations taken from the population of interest. For example, a population might be all apples in an orchard at a given time and we want to know how big the apples are. We can"t measure all of them so we take a sample of some of them and measure them. Inference is when we draw conclusions about the population from that sample. Because the sample was only a selection of objects from that population, it will never be a perfect representation of that population. Different samples of the same population will give different results. This is called sampling error or variation due to sampling.

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