This course consists of two parts: 1) experimental design and 2) data analysis. The experimental design part of the course largely follows a textbook (Ruxton and Colegrave 2016). The main theme of the data analysis part is the method of maximum likelihood although other approaches are also discussed. Computer simulations will be used to understand the concepts of various statistical methods, but no prior experience in programming is required. Although the course title contains the word ecology, this is a general course on experimental design and data analysis. Students of any fields (social science, political science, physical science, biological science, business, engineering, etc.) can take the course. No knowledge of ecology is required. On the other hand, students are expected to have the basic knowledge of statistics (e.g., one semester of an introductory statistics course). The computer language R (link) is used. Expectations

• Ask questions, in or out of class, when you don't understand something. If you are confused, you are probably not the only one.
• Assignments will be given nearly every week. Students must work on assignments independently.
• Some course materials (R scripts, optional readings, etc.) will be sent by email. Students are responsible for checking their NTU email accounts regularly.

• understand how typical statistical tests (e.g., null hypothesis significance testing) work.
• be able to program in R.
• be familiar with common statistical models (e.g., generalized linear [mixed] models) and methods.
• be able to build own statistical models when common models are not appropriate.