Click on an item below to see the definition
Sample:
A set of individuals chosen (usually randomly) from a larger population.
Sample size:
The number of individuals in a sample.
Sampling:
The process of drawing a sample of subjects from a population.
sd:
See Standard deviation.
se:
See Standard error.
Significance:
Significance is an English word that has been hijacked by statisticians.
In general usage the term significant difference means an important difference,
but "significant" in this sense is subjective. On the other hand, statistical
significance is objective, and is based on the concept "p<0.05". In an experiment,
a difference is detected by challenging a null hypothesis of "no difference".
When p is less than 0.05, the null hypothesis is rejected - when p is greater than
0.05, the null hypothesis is accepted. So what does p<0.05 really mean? A p
value of less than 0.05 means that there is even less than a 0.05 probability of
getting the observed test statistic if the null hypothesis is true. This is an
incredibly misunderstood concept, by even experienced users of statistics.
See also Probability.
Significance level:
The standard significance levels are 95% (p<0.05) and 99% (p<0.01).
Simple regression:
See Linefitting
Single-blind:
A double-blind study, in which neither subject nor evaluator knows what
treatment or regime has been administered, reduces the risk of bias (psychological or otherwise) being introduced
by either the investigator or the subjects of the study. Single-blind occurs when one of these two is aware of the
threatment or regime administered.
See also Blind study, Blinded evaluation, Double-blind.
Skewed:
Data which is not symmetrically distributed.
Slope:
Also called the gradient. The rate of increase in the vertical-axis variable
for a unit change in the horizontal-axis variable.
Standard deviation (sd):
The variance and its square root, the standard deviation, are the pre-eminent
statistics used to summarise how much variability there is in a sample or population.
Standard error (se):
Standard error is a kind of tolerance around the sample mean, and in many ways
the most important concept in statistics. It is related to, but not the same as, standard deviation. Essentially, if you could
take infinitely many samples of a particular size from a population, the means of the samples would themselves form a population.
The standard error is the standard deviation of this population of sample means. More formally this is called the standard error
of the mean. By the same token, there are standard errors or any estimated parameter, eg. the standard error of the intercept and
standard error of the gradient.
Subject:
This is used in statistics to mean an individual (rather than its more
usual meaning of a topic). It need not be a person - it depends on the discipline; an animal possibly.
