Sampling Distributions

These flashcards cover key concepts and definitions from the chapter on sampling distributions.

A sampling distribution is a distribution of all of the possible values of a sample statistic for a given sample size selected from a __.

population.

In developing a sampling distribution, the population size N is equal to __.

4.

The values of age (X) for the population are __, __, __, and __.

18, 20, 22, 24.

The summary measures for the population distribution are developed from __ samples.

possible.

In sampling distribution of means, the sample means distribution is no longer __.

uniform.

The Z-value for the sampling distribution of the mean is derived from: Z = ( - ) / ( __ / √n ).

sample mean; population mean; population standard deviation.

As sample size (n) __, the standard error decreases.

increases.

According to the Central Limit Theorem, as the sample size gets large enough, the sampling distribution of the sample mean becomes almost __.

normal.

For most distributions, a sample size greater than __ will give a sampling distribution that is nearly normal.

30.

In an interval that contains 95% of the sample means, 2.5% of the sample means will be above the __ limit.

upper.

For a population with a mean of μ = 8 minutes and standard deviation σ = 3 minutes and sample size n = 36, to find the probability of the sample mean between 7.8 and 8.2, we can use the __ limit theorem.

central.

The sampling distribution of the mean for a normal population is always __.

normally distributed.

Based on samples of size 25, the sample means in 95% of all samples are between and .

362.12; 373.88.

The number of different samples possible when sampling with replacement of size n = 2 from a population of size N = 4 is __.

16.

The sampling distribution of all sample means will be affected by the __ chosen from the population.

sample size.