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Appendix 5 Common terms (and some
of their alternatives) used in statistics
Standard abbreviations are shown in brackets. Note these are usually in italics,
which is tedious for typing, but that is the convention. (They do also vary
between textbooks, so check in the list of symbols you find in the book you are
using.) Words in italics have their own definition.
Examples of how to report the results of some of the common statistical tests are
given, under the test entry, in square brackets.
For more terms used in experimental design and methodology, see Appendix 4.
Alpha (α) level The level of probability at which you decide in advance the
null hypothesis can be rejected and you can announce that
you have a significant result; it is usually set at 0.001,
0.001 or 0.05; in behavioural experiments, where we
typically have a large variance, we tend to choose 0.05, the
easiest of these to achieve [p < .05]
Analysis of variance Probably the most common test used in psychology; use it
(ANOVA) when you have several variables or more than two groups
and you want to compare their means. [The duration of
eye contacts between individuals varied over the three
conditions, F(2, 177) = 4.37, p = .03. (In this example the
numbers 2 and 177 represent the degrees of freedom.).] Note
the ANOVA does not tell you which conditions produced
longer or shorter durations, just that there was a difference.
You can get some idea of the direction of the difference
by looking at the means, but to confirm that these
are significant you would have to use planned contrasts or
comparisons (if these were identified in advance) or post
hoc tests (if the comparisons were not planned in advance);
these compare each pair of means separately; ANOVAs
will also tell you whether interactions between variables
are significant, which is often the most interesting and
important part of the analysis