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The ability to evaluate data and assess scientific claims is a core skill that employers will look for. This is usually taught in the form of a 鈥渟tatistics course鈥�, but we take the view that statistics is only really useful in the wider context of evaluating data.

This course will begin with an introduction to the issues involved in evaluating data and scientific papers. Students will be taught good practice in presenting data, in the belief that the best way to learn to evaluate something is to do it oneself. Common pitfalls involved in interpreting graphs and tables will be described. Students will then learn about experimental design and basic statistics, such as measures of central tendency, distributions, and hypothesis testing. From this foundation, students will be taught how to mine literature databases efficiently and productively, and how to evaluate scientific papers, as well as how to contribute to discussions in the context of scientific talks and seminars, as these are situations where evaluative skills are put to the test under considerable time pressure.

Teaching employs several modalities including Moodle-based short talks covering much of the entire scope of the module, practice exercises and links to complementary external sites; small group discussion and learning; and practice-based workshops.

Students taking this module will need access to basic data analysis and graph-plotting software such as Microsoft Excel, for which the methods required in the module will be taught. Only a basic level of competence in mathematics is needed.