Statistical Analysis: Null-hypothesis Testing with R
ONLINE
This workshop will focus on developing theoretical and practical skills for null hypothesis testing and probabilities in R.
The session will begin with a brief overview of statistics, and go on to cover probabilities and hypothesis testing, including:
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T-tests, KS-tests and normalised data
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Null hypotheses and probabilities
The workshop will ultimately build on an understanding of basic statistical analysis and show how more complex statistical analyses and simulations can be used in R. The relatively advanced techniques and theory behind all of this are highly applicable to wider concepts and will allow you to apply and build on the core concepts with your own research.
This is an advanced-level workshop. A basic understanding of R and statistical analyses is assumed. This assumed understanding is at the level covered in the Introduction to Statistical Analysis session. Participation in that introductory session is advised before undertaking this session. The Tidyverse package should be downloaded and installed before the workshop.
Those who have registered to take part will receive an email with full details and a link to join the session in advance of the start time.
After taking part in this event, you may decide that you need some further help in applying what you have learnt to your research. If so, you can book a Data Surgery meeting with one of our training fellows.
More details about Data Surgeries.
If you’re new to this training event format, or to CDCS training events in general, read more on what to expect from CDCS training. Here you will also find details of our cancellation and no-show policy, which applies to this event.
If you're interested in other training on data analysis, statistics, and machine learning have a look at the following:
- Digital Method of the Month: Statistics
- Introduction to Statistics and Descriptive Statistics
- Finding Patterns Across Data
- Digital Method of the Month: Machine Learning
- Introduction to Machine Learning
- Statistical Methods: Montecarlo Simulations with R
- Systematic Data Cleaning in Python
- Regression and Mixed Effects Modelling with R
- AI and Ethics
- Statistical Methods: Principal Component Analysis with R
