Science is fundamentally about testing ideas, not collecting facts. But how do we test ideas? We do so by analysing numbers to reveal patterns, and then by designing experiments to exclude competing ideas that might explain what causes these patterns. In this course, you will discover how to design experiments and analyse data. You’ll then be equipped to critically assess scientific claims that you hear in the media. Think about how much of the news is about alleged medical breakthroughs, causes of health problems, and claims that one economic policy is better than another. We want to help you to sift out fake news, identify misleading ways to describe data and allow you to assess the importance of different factors in explaining the patterns we see in the world. This is a core life skill. And, naturally, if you take this course you’ll be better equipped to take the path to becoming a biologist yourself.

This course will explore the ways biologists design experiments, generate data and assess evidence using a variety of statistical techniques. We will discuss the value of different scientific approaches, including hypothesis-driven experiments and exploration of large-scale data generation–such as genome and transcriptome sequencing projects. We’ll consider examples from microbial, animal and plant biology, and you will carry out exercises to develop and test hypotheses, and critically interpret the results. You will have the opportunity to participate in all stages of a biological experiment. This will include the conception and design of a study, laboratory work, analysis of the data, drawing conclusions, writing the report and engaging graphical ways to present your findings. The R programming language will be taught and used throughout the course. Students will be required to use their laptop computers.

Note: Graduate students attend joint classes with undergraduates but are assessed separately.