Biometry Hub Internship

School of Agriculture, Food and Wine, University of Adelaide, Australia

Internship Program Structure


Week 1

  • Introduction To R Workshop
    • You are given a workshop on intro to R and provided with a set of guided material to follow, and data examples to play with.

You will follow the material, work on your computer, come up with solutions and discuss them with your coach.

  • Experimental Design Workshop
    • You will be also given an Experimental Design workshop and expected to manage basic designs in R after the workshop.
  • Attending Statistical Meeting
    • You are invited to a professional meeting, Stats@Waite, where you can meet statisticians working at Waite and expand your network.
  • Attending Seminar
    • You are invited to a seminar on ggplot2 and tidyverse.

Week 2

  • Meeting With Senior Biometricians
    • During this week, three senior statisticians are meeting with you over coffee(s) to talk about:
      1. Principles of statistical inference in practical applications.
      2. Laboratory variation.
      3. Principles of sampling designs.

You will take notes and start to understand and appreciate the role of biometricians and the importance of the biometry.

  • R VS Genstat
    • Code in R of the experiment that you have designed in the Research Methodology course

You will create a CRD/split-plot/RCBD design in R to reflect your experiment in the course.

  • Independent Reading
    • You will be reading a book named practical statistics and experimental design for plant and crop science.

You will take some notes as you read

Week 3

  • R VS Genstat
    • You will run and analyse the experiment in R using anova or lm4 package, and present your results to the group.
  • Trialling Theory In Practice
    • Another senior statistician will talk to you about the fascination of optimal design.

You will continue to read the book, practice the shiny app and prepare a talk/presentation/video on the ideas of simple random sampling/accuracy of experimental measurements/randomized design of controlled experiments/use of R/etc