Statistical Thinking

Statistical Thinking

statistics
Location:

Online course

Start date:

03 May 2021

Duration:

3, 4 and 5 May, each time from 9h00 - 12h00

General context

The course mission is to help researchers to see statistics as an intrinsic part of research. Not as a tool that provides standard output when given an input, but a mean to help integrate their knowledge to allow them making objective decisions. 

To achieve this, we should get rid of deeply embedded misconceptions about some common statistical tools and replace them by a more intelligent and flexible use of statistics based on understanding of the important principles.

A selection of important topics will be covered in dialogue with the course takers.

Event intended for

Researchers and research managers interested in improving their research by applying important statistical principles that go beyond applying standard tools without understanding them.

Extra information
  • This course if free for VIB participants
  • Non-VIB participants pay a fee of 50 euro
  • This course is not open for industry
  • Note that upon no-show without valid justification you will be blacklisted for the VIB training program for 1 year and a fee of €100 will be charged. Click here for more information

Trainers

Joris De Wolf

Joris De Wolf has a long track record in biostatistics. He worked at Crop-Design, later BASF, as team leader of the biostatistics group. He was involved in the experimental design and start-up of the high-throughput phenotyping system TraitMill, design of the databases and streamlining of statistical analyses. Furthermore, he was research responsible for the field testing pipeline and statistics on transgenic yield improvement. In 2016, he joined GSK vaccines and currently he also works as a biostatistics consultant. 

Program

  • Introduction
  • Case studies to make clear that simple application of statistics lead to problems
    • Discussion in subgroups
    • Plenary discussion
  • Place of statistics in research
  • Intelligent experimental design
    • Getting the context clear
    • Getting the research question clear
    • Deriving a design from that question
  • Case studies on reading and displaying results
    • Discussion in subgroups
    • Plenary discussion
  • More than only data: combining expert knowledge and statistics
    • Models
    • Interpretation
    • Conveying the message
  • Recap and putting things in the bigger context again
  • Closure