LAUSR

Recent developments in technology and computing are facilitating the collection and processing of large volumes of increasingly complex data, while apparently supporting easier access to advanced data analysis methodologies and cutting-edge algorithms that, superficially at least, are straightforward to use. While it is true that the technical aspect of statistical computation is dramatically simplified by access to software tools, the most important requirements for success are a genuine understanding of variation and uncertainty coupled with well-developed statistical knowledge and thinking. However, with the proliferation of systems for rapid, semi-unsupervised data processing and analysis, particularly in relation to new forms of complex high-throughput data, users are increasingly less likely to be aware of, understand or know how to assess the assumptions that new algorithms and advanced statistical methods are making, especially where these assessments are subjective and require use of other concepts and methods. Statistics as a discipline may be perceived by some researchers as a ‘necessary evil’, with which they engage to facilitate publication by scientific journals. Yet ethical considerations and the economic costs of animal experimentation should themselves be sufficient reasons to encourage the use of well-considered and rigorous statistical analysis to support reliable scientific conclusions. Many of the most serious issues that impact on scientific studies frequently arise from poor experimental designs, misunderstanding of statistical concepts and naive use of powerful computational resources. However, when reviewing scientific studies, it is paradoxical that although much weight is put on the statistical results – even, occasionally, to the extent of overlooking the actual biological relevance – the methods used and the rationale for key decisions regarding their use are often reported only in terms that are vague or even actively misleading. Quantitative methods should not be used blindly as a routine step in the research pathway without critical exploration and …