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The Distribution of Sample Means - Coggle Diagram
The Distribution of Sample Means
population: entire group
Population Standard deviation: measures spread of population scores
Average distance scores are from the population mean
Population Mean: average of the entire population
True average for the whole population
Distribution of sample Means
we have the original population of scores
the distribution of sample means
have a sample that is selected from the population
Standard Error
standard error of M: standard deviation of the distribution of sample means
Sample means: mean of all of the means sample
Sample mean: average of the scores
Sample size: number of people in a sample
law of large numbers: the larger the sample size the more probable it is that the sample mean will be close to the population mean.
Random selection: chance so everyone has an equal opportunity to be picked
Central limit theorem: any population with mean, and standard deviation that the distribution of sample means for sample size n will have a mean of and a standard deviation of and will approach a normal distribution as n approaches infinity.
describes the distribution of sample means by identifying the three characteristics
shape
n gets larger, the distribution of sample means will closely approximate a normal distribution.
central tendency
represents typical outcome
variability.
this is the less than population variability