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Statistics: Chapter Seven - Coggle Diagram
Statistics: Chapter Seven
Transforming a sample mean into a z-score
0 = Centralized sample; -2 or 2 = Extreme sample
Sampling Error: Discrepancy between sample & population parameters (natural variance)
Distribution of the Sample Mean: Sample means from all possible random samples from a population
Sampling Distribution: Distribution of statistics obtained by selecting all possible samples of a specific size from the population.
The sample means should pile around the population mean. (Normal shaped distribution)
Central Limit Theorem: Describes distribution of a given sample using shape, central tendency, & variability.
The shape of the distribution is normal if the population is normally distributed and there are a large number of scores.
Expected Value of M: The mean of the distribution of sample means is equal to the mean of the population of scores
Standard Error of M: Measure of how much distance is expected between a sample mean (M) and a population mean (μ)
The Law of Large Numbers: The larger the sample size (n), the more likely it is that the sample mean will be close to the population mean.
Standard deviation is the starting point for standard error
Standard Error: Tells researchers how accurately their sample data represents the population
Standard Error: Also tells how much of an error should exist between sample & unknown population mean
Population standard deviation / square root of sample size
Variability: Standard deviation of the distribution of sample means is called the standard error of M
Three Distributions: 1. Original population; 2. Selected sample; 3. Distribution of sample means
Probability is equal to proportion
New info: We are using distribution of sample means instead of scores & standard error instead of standard deviation
Reliability: Consistency of different measures of the same thing.