Click on an item below to see the definition

###### Random sample:

Every item in the population has the same chance of being selected
for a random sample, with no favouritism.

###### Randomisation:

The process of ensuring that a sample is truly random. There are many
ways of doing this, and to different levels. The entire sample should be chosen according to some strategy
that does not unduly favour any particular group of individuals. A further level ensures that the subjects
already chosen for the study are randomly allocated to the treatments of the study. This removes any suspicion
that unscrupulous experimenters ensure that their particular hobby horse is tried out on the most susceptible
subjects.

###### Range:

The difference between the maximum and minimum values in a sample
or population.

###### Rank:

The data is sorted into numerical order and each value is given a rank
according to its position. This is the basis of most non-parametric tests.

###### Regression:

Also known as linefitting. A method
that finds the best 'line' through a set of plotted points, used to model an
outcome variable in terms of a linear combination of predictor variables (also
called independent variables).

*See also Multiple regression*.

###### Relative risk:

For instance, the ratio of the risk of disease in one group compared
to another group.

###### Relevance:

Getting statistical significance is not necessarily the end of the story.
In any experiment you would also want to see practical significance, which is not always present. In practical
terms, the statistically significant difference may be so small as to be irrelevant. Noe that we may see practical
significance in a sample, but we cannot conclude that this is a real effect in the population unless we also have
statistical significance.

###### Residual:

The difference between the observed value and the value predicted by the model
under investigation. In a linefit situation, this would be the vertical difference between the line and the
actual point.

###### Retrospective study:

In a retrospective study you work backwards from a condition (eg. disease) to the
causal factor. For example, how many of those first-time prisoners who re-offended within a year of release served sentences
at a liberal prison as opposed to a traditional prison?

*See also Prospective study.*

###### Risk:

The proportion succumbing to disease in a group, for example.