Incluye referencias bibliográficas e índices

Why you need to read a little philosophy first: the philosophy of science governs the practice of science -- Defining scientific research -- Why do science? What is science for? The need for inductive reasoning -- The first step to engaging in scientific research: establishing your framework -- A short history of philosophy relevant to scientific method: how we got to where we are today and the problem of induction -- The sky is red hypothesis: examining the hypothesis-falsification framework -- The hypothesis as a framework for scientific projects: is critical rationalism critical enough? -- Scientific settings in which a hypothesis-falsification framework is not feasible -- The question and the model: forming an inductive framework for scientific projects (by getting to Carnegie Hall) -- Advantages to the question/model-building inductive framework -- A biological example of the question/model-building framework -- Some concluding remarks on the philosophy of experimentation: warnings and exhortations -- The system -- System validation -- Choice of a model organism or technique: validation experiments -- System validation requirements for distinct experimental readouts -- System specificity: specificity of detection and specificity of perturbation -- System sensitivity: minimizing signal to noise to improve sensitivity of detection -- System stability -- Determining conditions to measure efficacy -- System validation: determining conditions to measure safety -- Definition of the experiment: the framework for an individual experiment -- The negative control: distinct types -- The requirement for the positive control -- Method and reagent controls -- Subject controls -- Assumption controls -- Experimentalist controls: establishing a claim to an objective perspective -- Biological replicates, technical replicates, experimental repeats, time courses (repeats over time), and dose responses -- Summary of the components of the individual experiment -- Building the model: representations of the experimental data -- Data filtration -- Model induction: asking follow-up questions and finishing the project by writing the manuscript -- A short synopsis -- Designing the experimental project: a biological example.