# 2.3. Appropriate worker and cage replicates for laboratory experiments involving adult workers

A minimum
sample of 30 independent observations per treatment is relatively robust for
conventional statistical analyses (e.g. Crawley, 2005); however, financial constraints
and large effect sizes (e.g. difference among treatments for the variable(s) of
interest; see statistics paper (Pirk *et
al.* (2013)) will no doubt lower this limit, especially for experiments
using groups of caged workers. Larger sample sizes (i.e. number of cages and
workers per cage) reduce the probability of uncontrolled factors producing
spurious insignificance or significance, and help to tease apart treatments
with low effect size. Repeated sampling of individuals over time to observe
development of parasite infection, for example, will also require larger
samples.

Furthermore, it is important to
consider biological relevance of the numbers of individuals in each cage.
Unsurprisingly, isolated workers die much quicker than those maintained in
groups, possibly due to timing of food consumption (Sitbon, 1967; Arnold, 1978),
so experimenters must be aware of expected duration of survival. Possible
individual and social behaviours that are of interest should also be considered
(e.g. Beshers *et al., *2001). For
example, >75 workers were needed to consistently elicit clustering behaviour
(Lecomte, 1950), whereas 50 workers and a queen were needed for the initiation
of wax production (Hepburn, 1986).

A Monte Carlo simulation model
incorporating average lifespan (and standard deviation) for treatments and
controls has been created to determine percentage of cases where a significant
difference is obtained between groups. Without preliminary trials to determine
the magnitude of an effect elicited by an experimental treatment as well as the
variation between cages in that effect, statistical power may be impossible to
know in advance. In such cases, it is advisable to maintain as many cages per
treatment (≥3) and individuals per cage (≥30) as possible. Examination of the
literature for similar studies may also help choose sample size; however,
caution should be exercised due to differences in experimental conditions. Refer
to the *BEEBOOK *paper on statistical methods
(Pirk *et al.*, 2013) for further
details on the Monte Carlo simulation and on selecting appropriate sample
sizes.

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