Module 1, Lecture 1 SUMMARY: - Must make questions explicit, and state hypotheses and logical predictions - Correlative approach will tell you that a pattern exists, but (in most cases) only an experiment will tell you why - A simple framework can make life easier!

Module 1, Lecture 2 By the end of this lecture you should understand: - Biology (marine, intertidal, terrestrial) is a question-based discipline - Correlative vs Experimental approach - Falsificationist approach - A LOGICAL FRAMEWORK

Module 1, Lecture 3 By the end of this lecture you should understand: - The need for random but representative samples - In heterogeneous environments, stratification is important - That precision can be improved by stratification

Module 1, Lecture 4 By the end of this lecture you should understand: - The need for proper controls in experiments o Effectively for everything you do! - The need for interspersion of treatments

Module 1, Lecture 5 By the end of this lecture you should understand: - Carrying out a t-test - One- and two-tailed t-tests

Module 1, Lecture 6 By the end of this lecture you should understand: - Why we need ANOVA - Entering data and running a 1-way ANOVA - Interpreting a 1-way ANOVA - Entering data and running a 2-way orthogonal ANOVA - Interpretation of one of those ^^

Module 1, Lecture 7 By the end of this lecture you should understand: - Why independence is important - Lack of independence causes pseudoreplication - Lack of independence causes confounding - Independence is the single most important thing you can use to do good experiments (both manipulative and mensurative

Module 1, Lecture 8 No lecture objectives, but title = "Counting things and ascribing relationships"

Module 1, Lecture 9 AIM: To learn tests of independence for categorical data