M (sample mean) is an unbiased estimator of (population mean) // this statement means that on average, the average sample mean will be equal to the population mean. M specifies the difference between m and due to chance. Z-score test quantifies inferences about z = m- / m. = obtained difference between data h0 / expected difference due to chance. For normally distributed z-scores, use the unit normal table to determine critical z required to reject h0 z vs. t standard error ( m) To use z, population variance ( 2) must be known to calculate z = m- / m solution use sample variance (s2) to estimate ( 2) then use s2 to estimate standard error. More often than not, 2 is unknown. To test hypotheses when 2 is unknown use t-test. Determine critical value of t using the t distribution table. Sm = s2/n t = m / sm.