Click on an item below to see the definition
Baseline:
The initial data score of a subject before an experiment,
used as a benchmark (or reference value) against which the experimental data is compared.
Bias:
Bias is a consistent error brought about by experimental design
favouring one group over another or by the investigator/data recorder favouring one group over another.
In the first case it can be prevented by matching the groups, and in the second case by blinding.
See also blind study.
Binary data:
Data that can take only one of two
values, eg. Yes/no, on/off, dead/alive.
See also Data types.
Biological significance (relevance):
Getting statistical significance is not necessarily the end
of the story. In a biological experiment you would also want to see biological significance, which is not
always present. In biological terms, the statistically significant difference may be so small as
to be irrelevant. Note that we may see biological significance in a sample, but we cannot conclude
that this is a real effect in the population unless we also have statistical significance.
Blind study:
Because human psychology plays a big part in how we respond
to things, blinding may be employed to ensure that subjects in an experiment do not know which of the
treatments they are receiving. This is used to combat bias. For example, in a clinical trial people's
beliefs could affect the outcome; thus non-drug treatment may appear to be more effective on advocates
of alternative medicine.
See also bias, blinded evaluation, double blind, single blind.
Blinded evaluation:
Unfortunately sometimes even investigators have biases (though
they may not realise it). Therefore it is better if they do not know which treatment a particular subject
received. In a blinded evaluation an investigator reviews and assesses outcome without knowing which
particular treatment has been applied. This eliminates the possibility of both conscious and unconscious
bias.
See also bias, blind study, double blind, single blind.
Block randomisation:
A randomisation techique often used in multi-centre clinical trials
where each block contains a unique treatment allocation. The blocks are then allocated randomly in centres.
Bonferroni:
When multiple testing is carried out the likelihood of finding a
significant difference increases in line with the number of tests. The bonferroni correction is applied to the
significance level of each test to ensure that the overall significance level (across the complete set of
tests) is brought back to the required level (usually 0.05). The correction stipulates that the significance
level of each test is 0.05 divided by the number of tests.
See also probability, significance level.
Box and whisker plot:
A graphical display showing the range of the middle 50% of data within
a box, with two extending lines (the whiskers) indicating the upper and lower extremes.
