February 15-18, 2009 Washington State Convention Center Seattle ...

ON CHOICE AND USE OF STATISTICAL TOOLS IN EXPERIMENTAL FISH NUTRITION
STUDIES
Juhani Kettunen and Kari Ruohonen
Finnish Game and Fisheries Research Institute
Viikinkaari 4
FIN-00791 Helsinki
Finland
juhani.kettunen@rktl.fi
The main aim of the paper is to discuss and demonstrate the sequence of decisions that are necessary when using statistical
models in fish nutrition studies. We emphasize the key role of statistical experimental design that is, unfortunately, too often
flawed or completely missing in present studies. Furthermore, we demonstrate the choice of model that is based on the scale
and distribution of response and predictor variables. We recommend an iterative experimental strategy. Thus, instead of a single
one-shot experiment we prefer a set of consecutive, complementary experiments that are designed on the basis of existing
knowledge and outcome of the preceding trials. The philosophy behind is to start from screening the relevant variables with
simple and cost-effective designs and proceed towards more detailed understanding of the system with more targeted and often
more complicated designs. We speak of model-based design and analysis of fish nutrition problems. By doing this we design
our experiments to minimize the posterior uncertainty of the model parameters and forecasts. For this the choice of a model on
the basis of the scale and distribution of response and predictor variables is crucial. In the paper, we compare the model-based
approach with the traditional design-based approach that emphasizes statistical hypothesis testing in finding significance of effects.
We demonstrate many strengths of model-based approach in comparison with design-based one. Most important strength
of the model-based strategy is the information that is given to decision makers, i.e. users of the results. While the design based
approach gives him/her a on-off solution, the model-based leaves the final decision to the decision maker. This is possible,
because also estimates of the risk and uncertainty levels are produced.
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