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What is a paired sample in statistics?
Paired samples (also called dependent samples) are samples in which natural or matched couplings occur. This generates a data set in which each data point in one sample is uniquely paired to a data point in the second sample. The “opposite” of paired samples is independent samples.
What does paired data mean in stats?
Generally this would be data sets where every data point in one independent sample would be paired—uniquely—to a data point in another independent sample. This might be because they come from the same observational unit; the same individual, or the same location.
Why are there multiple measures of a distribution?
There are multiple measures because there are different ways to think about what is the “center” of a distribution. Each measure has pros and cons and will be useful in different situations. As a result, you need to provide all three measures to give a full description. The mean is the arithmetic average of all of the scores in your distribution.
How are the differences in a matched sample calculated?
Either the matched pairs have differences that come from a population that is normal or the number of differences is sufficiently large so that distribution of the sample mean of differences is approximately normal. In a hypothesis test for matched or paired samples, subjects are matched in pairs and differences are calculated.
What do you need to know about distributions in statistics?
When describing distributions on the AP® Statistics exam, there are 4 key concepts that you need to touch on every time: center, shape, spread, and outliers. Below is a preview of the main elements you will use to describe each of these concepts. In the following sections, we’ll explain each of these terms one by one.
How to describe the features of a distribution?
CO-4: Distinguish among different measurement scales, choose the appropriate descriptive and inferential statistical methods based on these distinctions, and interpret the results. LO 4.7: Define and describe the features of the distribution of one quantitative variable (shape, center, spread, outliers).