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# Test #2 - Chapter 7 sampling error = the discrepancy or...

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Chapter 7 sampling error = the discrepancy, or amount of error, between a sample statistic and its corresponding population parameter sampling distribution = a distribution of statistics obtained by selecting all the possible samples of a specific size from a population - the distribution of sample means will be almost perfectly normal if one of the two conditions is satisfied 1. the population from which the samples are selected is a normal distribution 2. the number of scores (n) in each sample is relatively large, around 30 or more - central limit theorem: for any population with mean μ and standard deviation σ , the distribution of sample means for sample size n will have a mean of μ and a standard deviation of σ / n and will approach a normal distribution as n approaches infinity - standard error: the standard deviation of the distribution of sample means measures the average distance between M and μ that is reasonable to expect just by chance the magnitude is determined by two factors 1. the size of the sample (as n increases, s.e. decreases) 2. the standard deviation of the population from which the sample size is selected - z-score describes a sample mean’s position in the distribution of samples Chapter 8 null hypothesis = states that in the general population there is no change, no difference, or no relationship - predicts that the independent variable (treatment) will have no effect on the dependent variable for the population alternative hypothesis = states that there is a change, a difference, or a relationship for the general population - predicts that the independent variable (treatment) will have an effect on the dependent variable for the population critical region = composed of extreme sample values that are very unlikely to be obtained if the null hypothesis is true -

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