Statistics Terminology
Error Types
 Indeterminate (random) error: evaluate with statistics.
 Determinate (systematic) error: evaluate with reference standards.
 Gross error: big mistake, like spilling everything on the floor.
Probability
 One sided probability: Use one sided probability if comparing the size or magnitude from two different data sets (ie. a is larger than b).
 Two sided probability: Use two sided probability if comparing two different data sets for a difference (ie. a is different than b).

 Population: This refers to a set of all possible measurements. This is an ideal that can only be approached. Greek letters are used to symbolize population statistics.
 Sample: This refers to a set of actual measurements. The distinction between sample and population statistics is most important for a small number of measurements (less than 20).

 tTest: This is one of the most powerful and widely used statistical tests. The t test (Student's t) is used to calculate the confidence intervals of a measurement when the population standard deviation ( ) is not know. Which is usually the case. The ttest is also used to compare two averages. The ttest corrects for the uncertainty of the sample standard deviation (s) caused by taking a small number of samples.
 QTest: This test is used to determine if there is a statistical basis for removing a data point from a data set.

 Detection Limit: The noise is equivilant to the standard deviation of the blank. The signal to noise ration (S/N) is just the signal divided by the noise.
 Action Limit; Lc 2 or S/N = 2. At the action limit you are 97.7% certain that signal observed is not random noise.
 Detection Limit; LD 3 or S/N = 3. At the detection limit you are 84% certain to detect signal (it is above the action limit) if the analyte is at this concentration.
 Quantitation Limit; LQ 10 or S/N = 10. Sample concentration required to give a signal with 10% RSD.
 Type I Error; Identification of random noise as signal.
 Type II Error; Not identifying signal that is present.