The Art of Statistics: Learning from Data

by David Spiegelhalter

These ‘rules’ should be fairly self-evident, and rather neatly summarize the issues tackled in this book. Statistical methods should enable data to answer scientific questions: Ask ‘why am I doing this?’, rather than focusing on which particular technique to use. Signals always come with noise: It is trying to separate out the two that makes the subject interesting. Variability is inevitable, and probability models are useful as an abstraction. Plan ahead, really ahead: This includes the idea of pre-specification in confirmatory experiments – avoiding researcher degrees of freedom Worry about data quality: Everything rests on the data. Statistical analysis is more than a set of computations: Do not just plug into formulae or run procedures in software, without knowing why you are doing so. Keep it simple: The main communication should be as basic as possible – do not show off skills in complex modelling unless they are really necessary. Provide assessments of variability: With the warning that margins of error are generally bigger than claimed. Check your assumptions: And make clear when this has not been possible. When possible, replicate!: Or encourage others to do so. Make your analysis reproducible: Others should be able to access your data and code.

Good sttistics based science rules