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Probability and Samples - Coggle Diagram
Probability and Samples
Sampling Error: Natural discrepancy, or error, between a sample statistic and its corresponding population parameter.
As the sample size increases, the error between the population mean and sample mean should decrease. This is known as the Law of Large Numbers
Relationship between sample size and standard error: 
Distribution of Sample Means: the collection of sample means for all the possible random samples of a particular size that can be obtained from a random sample.
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Sampling distribution is a distribution of statistics obtained by selecting all the possible samples of a specific size from a population.
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Central Limit Theorem states that taking any population with a mean and standard deviation and using a large enough sample will always result in a normal distribution.
Shape will be normal if:
- the population from which the samples were selected has a normal distribution.
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Variability: Identified through the standard deviation of sample means and is referred to as the standard error of M*
Describes the distribution of sample means providing a measure of how much difference is expected from one sample to another.
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Calculated using formula:
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