Chp7 - Sampling Distributions

parameter

a # that describes the population. it is a fixed #, but in practice we don’t know it

statistic

a # that describes the sample. known # but can change sample to sample → used to estimate parameter

mean parameter

μ (population)

mean statistic

x bar (sample)

stand deviation parameter

σ (population)

stand deviation statistic

Sx (sample)

proportion parameter

p (population)

proportion statistic

p hat (sample)

variance parameter

σ² (population)

variance statistic

sx² (sample variance)

biased

consistently overestimates or underestimates the true population parameter

unbiased

mean of sampling distribution = population parameter

statistic/sampling mean = parameter/population mean

M of x bar = M

As n (sample size) increases…

the variability of the sample distribution decreases

a good stat has

1. a low bias (accurate)

2. a low variability (precise) (so high sample size)

accuracy means

true to intention

precision means

true to itself

the variability of a stat is described by

the spread of tis sampling distribution. this spread is determined by the sampling design & the size of the sample

• larger sample = smaller spread