# 3.1.3. Effect size

A third factor affecting decisions about sample size in experimental design is referred to as effect size (Cohen, 1988). As an illustration, if experimental treatments with a pesticide decrease honey bee food intake to 90% that of controls, more replication is needed to achieve statistical significance than if food intake is reduced to 10% that of controls (note that one’s objective should be to find biologically meaningful results rather than statistical significance). This is because treatment has a greater effect size in the latter situation. Effect size and statistical significance are substantially intertwined, and there are equations, called power analyses (see section 3.2.1.), for calculating sample sizes needed for statistical significance once effect size is known.

Without
preliminary trials, effect size, and also statistical power, may be impossible
to know in advance. If one’s objective is statistical significance, and one
knows effect size, one can continue to sample until significance is achieved.
However, this approach is biased in favour of a preferred result. Moreover, it
introduces the environmental influence of time; results one achieves in spring
may not be replicated in summer e.g*.*
Scheiner *et al*., (2003) reported
seasonal variation in proboscis extension responses (previously called
proboscis extension reflexes; also see Frost*
et al.*, 2012). Removing the
influence of time requires that one decides in advance of replication, and
accepts results one obtains. Without preliminary trials, it will always be
preferable to maintain as many properly randomised cages as possible. A related
factor that will influence sample size is mortality rate of honey bees in cages;
if control group mortality rates are 20% for individual bees, one will want to
increase the number of bees by at least 20%, and even more if variability in
mortality rates is high. Alternatively, without knowledge of effect size, one
should design an experiment with sufficient replicates such that an effect size
of biological relevance can be measured.