My interest in statistics is definitely pedestrian: I don't sit around doing statistical analysis with SPSS or SAS.
Assume a set of numbers, X:
X = {x1, x2, ..., xN}
Large naturally occurring sets of random numbers tend to have a normal distribution (aka Gaussian distribution; the famous bell curve).
The mode is the most frequently occurring value in the set of numbers, X. EG:
If X = {5, 6, 8, 9, 9}, then 9 is the mode.
The median is the nth of set of 2n values most frequently occurring value in the set of random variables, X. EG:
If X = {5, 6, 8, 9, 9}, then 8 is the mode.
The mean (aka average), x̄, is the sum of the set divided by the count of the set:
x̄ = (x1 + x2 + ... + xN) / N
If X = {5, 6, 8, 9, 9}, the mean is:
x̄ = (5 + 6 + 8 + 9 +9) / 5
x̄ = 37 / 5
x̄ = 7.4
The standard deviation, σ, is the "average" of the differences of each number in the set from the mean. Standard deviation is the root mean square deviation from the mean.
σ = sqrt( summation( square( xi - x̄ ) ) / N )
If X = {5, 6, 8, 9, 9}, then the standard deviation can be calculated:
- Find the differences from the mean for each number in the set:
{5-7.4, 6-7.4, 8-7.4, 9-7.4, 9-7.4} =
{-2.4, -1.4, -0.6, 1.6, 1.6}- Square each number in the set:
{-2.4*-2.4, -1.4*-1.4, -0.6*-0.6, 1.6*1.6, 1.6*1.6} =
{5.76, 1.96, 0.36, 2.56, 2.56}- Sum the set:
5.76 + 1.96 + 0.36 + 2.56 + 2.56 =
13.2- Find the square root of that:
sqrt(13.2) =
3.63318
(The variance, σ2, is simply the square of the standard deviation.)
By math magic (which I won't show here), for a normal distribution, the numbers in a set will be distributed as follows:
See also this nice chart from Statistics [W]:

A standard error uses the standard deviation in relation to the sample size. That is the greater the sample, the smaller the standard error.
standard error = σ / sqrt(samples)
If X = {5, 6, 8, 9, 9}, then the standard error can be found:
3.63318 / sqrt(5) =
3.63318 / 2.236067977 =
1.624807681
Page Modified: (Hand noted: 2007-10-23 23:39:34Z) (Auto noted: 2007-11-17 06:23:22Z)