# 9. Conclusion

Guidelines and the selection of the different
methods presented are, at least partly, based on experience and we cannot cover
all statistical methods available, for example we have not discussed resampling
methods like jackknife in detail (for further reading see Good, 2006). More details
on designing specific experiments and performing statistical analyses on the
ensuing data can be found in respective chapters of the COLOSS *BEEBOOK *(e.g. in the toxicology chapter,
Medrzycki* et al.*, 2013).

Experimenters need
to use statistical tests to take (or to help take) a decision. A statistical
analysis can be conducted only if its assumptions are met, which largely
depends on how the experiment was designed, defined during the drafting of the
study protocol. Without some effort at the *a priori* conception stage and
input from those knowledgeable in statistics and/or experimental design, the
resulting analyses are frequently poor and the conclusions can be biased or
flat-out wrong. Why spend a year or more collecting data and then realise that,
due to poor design, it is not suitable for its original purpose, to test the
hypotheses of interest. The most important point to understand about statistics
is that one should think about the statistical analysis before collecting data
or conducting the experiment.