Animals are still being used to teach, among other things, factual knowledge, skills and data handling in biomedical sciences. The learning goals of these laboratory classes need careful consideration, and may lead to the conclusion that alternative methods, not involving the use of animals, may be more appropriate to reach the learning goals. An overview is given of the different types of alternatives and sources where information on alternatives can be found.

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There is a wide range of humane and innovative tools and approaches used in veterinary education and training. Such new methods have been developed by teachers and trainers for pedagogical, economic and ethical reasons. The aim is to create the best quality education, ideally supported by validation of the efficacy of particular tools and approaches, while ensuring that animals are not used harmfully and that respect for animal life is engendered within the student. Veterinary education and training has not always met, and still often does not meet, this essential criterion. In this paper, we review tools and approaches that can be used to enhance education and training and to ensure the humane treatment and care of animals that must be observed as the very ethos of the veterinary profession. A review of the resources offered by the International Network for Humane Education (InterNICHE) is also presented.

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Sensitivity, specificity and accuracy are well known measures for evaluating the relevance of an inter-laboratory validation study for alternative tests. It is not generally discussed that the measures are dependent on two determining factors: a set of chemicals and the number of laboratories. Furthermore, some alternative tests such as these for the phototoxicity test have an "Equivocal" category for judging the toxicity of chemicals. These facts have made it difficult to interpret the value of the measures.
Therefore, in this paper we propose new measures to evaluate the alternatives, which depend on a set of chemicals rather than on both factors, and can treat data which have "Equivocal" category. We also propose their confidence intervals, which are measures of their precision.

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