Asample is selected from a population with m = 50 and s = 12. if the sample mean of m = 56 produces a z-score of z = +1.00, then how many scores are in the sample? 2 4 9 16

Law of Large NumbersLarge samples will be representative of the population from which they are scoredSampling ErrorNatural discrepancy, or amount of error, existing between a sample statistic and its corresponding population parameterSampling Error- a consequence of the fact that samples vary in their estimates of the population mean
- expected, and why we use random, and repeated sampling of populationsDistribution of Sample MeansThe set of sample means from all the possible random means populationDistribution of Sample MeansThe collection of sample means for all the possible random samples of a particular size (n) that can be obtained from a populationDistribution of Sample MeansThis is NOT a distribution of scores (X's), rather a distribution of statistics, means in particular (M's).Sampling DistributionDistribution of statistics obtained by selecting all of the possible samples of a specific size from a populationSampling Distributiongeneralized version of distribution of sample meansSampling Distributionconstructed by conducting several random samples of a population, each of size n, calculating the M of each of the samples, and plotting them in a frequency distributionsample meansIf we construct a Sampling Distribution correctly, then...

Our should pile around our population meannormalIf we construct a Sampling Distribution correctly, then...

Our sample means should form a dsitributionlargerIf we construct a Sampling Distribution correctly, then...

the the sample size, the closer the means should be to the population meanCentral Limit Theoremfor any population with a mean μ and a standard deviation σ, the distribution of sample means for the sample size n will have a mean of μ and standard deviation of σM = σ /√(n)Central Limit TheormThe holds for the distribution of any population, no matter the original shape, mean, or standard deviationdistribution of meansThe approaches normal very rapidllyThe Expected Value of Mthe mean of the sample distribution of mean, and is taken to be equal to the population μσMThe standard deviation for the distribution of sample means. Referred to as the standard error of MσMThis provides a measure of how much distance is expected, on average, between the sample mean M and the population mean μdescribesStandard error of M:

similar to the standard deviation, it the distribution of sample meansrepresentsStandard error of M:

standard error measures how ell and individual sample mean the entire error of M:

the larger the sample size, the the standard errorprobabilitythe distribution of sample means finds the associated with a specific sampleStandard Deviationmeasures standard distance between a score and a population meanStandard Errormeasures the standard difference between a sample mean and the population meanstandard deviationsWhat can approximate a measure of variability, when working with distributions of scores?standard errorWhen you have questions concerning samples, what is the appropriate measures of variabilitywill notSample error operates on the idea that a sample (will/will not) provide a perfect representation of a populationstandard errothe error between the estimates of the samples taken from the population, or average distance between a sample and population meanstandard errorWhat provides a measure for both measuring and defining the sampling error for a distributionInferential Statisticsmethods that use sample data as the basis for drawing general conclusions about the population