Definitions

Accuracy: A measure of how close the experimental result is to the "true" value

Precisison: A meauser of close the result is determined without knowing the true vaule; Precision is often used to predict the accuracy of a quantity to be measured (you don't know the answer before doing the experiment)

Random Error: The error in a result due to the finite precision of an experiment; A measure of the statistical fluctuations which result after repeated experimentation

Systematic Error
Reproducable inaccuracies introduced by faulty equipment, calibration, or technique.

Probability Distributions

Binomial distribtuion: best example is a coin toss, its either heads or tails; mean ([math]\displaystyle{ \mu }[/math]) = number of tries [math]\displaystyle{ n }[/math](coin flips) * probability of success[math]\displaystyle{ p }[/math] (head, 1/2); standard deviation([math]\displaystyle{ \sigma }[/math]) = [math]\displaystyle{ np(1-p) }[/math]

Poisson Distribution: standard deviation ([math]\displaystyle{ \sigma }[/math]) = root of the mean ([math]\displaystyle{ \sqrt{\mu} }[/math]); use in counting experiments; the distribtuion approximates the Binomial Distribution for the special case when the mean ([math]\displaystyle{ \mu }[/math]) is a lot less than the number of attempts to measure ([math]\displaystyle{ n }[/math]) because the probability of the event occurrring is small.; In the cosmic ray telescope experiment the mean number of detected cosmic rays is much smaller than the number of cosmic rays passing by.

Gaussian/Normal Distribution: Full WIdth at Half Max (FWHM) = width of the distribution at half the value of the maximum probabilty (distibution peak) = [math]\displaystyle{ \Gamma }[/math]; standard deviation ([math]\displaystyle{ \sigma }[/math]) = [math]\displaystyle{ \frac{\Gamma} {2.354} }[/math]; error = [math]\displaystyle{ 0.675 \sigma }[/math]