Unit 1

Introduction

Welcome to Week 1 of BIO144! Week 1 is a bit different from other weeks because its focuses on introducing how the course is going to work, collecting some data for us to use, and reviewing some material that it would be good for you to remember from previous courses (or learn here if you don’t know it yet!)

In subsequent weeks we will focus more on the data analysis concepts and practice.

However, just like all weeks, there is:

  • A chapter of the course book.
  • A “lecture” on Monday.
  • Some homework to ideally do between Monday and the practical on Thursday or Friday.
  • A practical session on Thursday or Friday.
  • A weekly quiz to check your understanding.

Course book

This week we will be covering Chapter 1: Introduction of the course book.

Code walkthrough

The R code of this week’s chapter is in a code walkthrough script, with a video walkthrough of it. Both are linked at the top of the course book chapter. Work through them before the practical: the last section of the script (“Practical toolbox”) shows code that this week’s practical uses but the chapter does not.

Learning objectives

These learning objectives, together with the content of the course book chapter, define what you need to know for the exam.

After this unit, students will be able to:

  1. Describe how the lectures, course book, practicals, homework, weekly quizzes and final exam of BIO144 fit together. (not examined)
  2. Identify the prior knowledge and skills assumed at the start of the course, and where to revise them. (not examined)
  3. Describe the steps of a data analysis workflow, from formulating a question to communicating the answer, and explain why the presentation and analysis should be planned before the data are collected.
  4. Describe the four principles for high-quality data analysis: a focused question with planned sampling and analysis; a model that accounts for the distribution and dependence of the data; emphasis on effect sizes and biological relevance; and reporting that makes the research reproducible.
  5. Explain the advantages of doing a data analysis with a script in R, such as being repeatable, readable and sharable.
  6. Distinguish appropriate from inappropriate uses of AI assistants in learning data analysis and in BIO144 assessments.
  7. Recognise and use the course notation for samples, populations and linear models (e.g. the sample mean, sample and population standard deviation, parameter estimates and residuals).

Lecture

Here are the slides for the lecture..

In the lecture you’ll be asked to record some reaction time data about yourself. Use this google form to record the data: Link to Reaction Time Data Form

Go to this web page for a tool to record your reaction times: Link to Reaction Time Tool

After the lecture, the class dataset will be available as reaction_times_2027.csv at this link (the Unit 1 practical shows how to download it straight into your RStudio project): https://raw.githubusercontent.com/opetchey/BIO144_Practicals_WAs/refs/heads/main/assets/datasets/reaction_times_2027.csv

Homework, Practical, and Weekly Quiz

Available here. You will need to be on a UZH network to access this link, so if you are not on campus, you will need to connect to the UZH VPN first.