Understanding the underlying principles of statistical techniques and effectively applying statistical methods can be challenging for researchers at all stages of their career. This concise, practical guide uses a simple, engaging approach to take scientists and clinicians working in laboratory-based life science and medical research through the steps of choosing and implementing appropriate statistical methods to analyse results. The author draws on her extensive experience of advising students and researchers over the past 30 years, breaking down complex concepts into easy-to-understand units. Practical examples using free online statistical tools are included throughout, with illustrations and diagrams employed to keep jargon to a minimum. Sample size calculations and considerations are covered in depth, and the book refers to types of data from experiments that clinicians and lab-based scientists are likely to encounter. Straightforward, accessible and encouraging throughout, this is a go-to reference for researchers who want to achieve statistical autonomy.
Understanding the underlying principles of statistical techniques and effectively applying statistical methods can be challenging for researchers at all stages of their career. This concise, practical guide uses a simple, engaging approach to take scientists and clinicians working in laboratory-based life science and medical research through the steps of choosing and implementing appropriate statistical methods to analyse results. The author draws on her extensive experience of advising students and researchers over the past 30 years, breaking down complex concepts into easy-to-understand units. Practical examples using free online statistical tools are included throughout, with illustrations and diagrams employed to keep jargon to a minimum. Sample size calculations and considerations are covered in depth, and the book refers to types of data from experiments that clinicians and lab-based scientists are likely to encounter. Straightforward, accessible and encouraging throughout, this is a go-to reference for researchers who want to achieve statistical autonomy.
