Statistics Terminology

  1. 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.

  2. Probability

  3. Groups

    • 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).

  4. Tests

    • t-Test: 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 t-test is also used to compare two averages. The t-test corrects for the uncertainty of the sample standard deviation (s) caused by taking a small number of samples.

    • Q-Test: This test is used to determine if there is a statistical basis for removing a data point from a data set.

  5. Limits

    • 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.