Within this module we will describe some key statistical concepts, and how to use them to make your research more reproducible. This module is a part of the training curriculum from the ReproNim (Reproducible Neuroimaging) Center.
The template for all ReproNim modules is based on the templates of Neurohackweek, Data Carpentry and Software Carpentry workshops. It is based on the lesson template used in Neurohackweek, Data Carpentry and Software Carpentry workshops.
| 09:00 | Module overview |
Who is this module for ?
How can I get some help if I get stuck while solving an exercise or a question ? How can I validate this module ? |
| 09:10 | Statistical basis for neuroimaging analyses: the basics |
Sampling, notion of estimation : estimates of mean and variances
Distributions, relation to frequency, PDF, CDF, SF, ISF Hypothesis testing: the basics H0 versus H1 Confidence intervals Notion of model comparison : BIC/Akaike Notion of bayesian statistics |
| 09:10 | Effect size and variation of effect sizes in brain imaging |
Variance explained
What is an effect size, statistical versus biological or medical relevance Why effect sizes vary: sampling, models, processing parameters, population, effect of unknown parameters Other measures of effect size |
| 14:10 | P-values and their issues |
What is a p-value ?
What should I be aware of when I see a ‘significant’ p-value ? |
| 14:10 | Statistical power in neuroimaging and statistical reproducibility |
What is power
Why is it important: issues with low statistical power Some tools for power within neuroimaging |
| 17:10 | The positive Predictive Value | What is the positive Predictive Value (PPV) ? |
| 18:10 | Cultural and psychological issues | What have we learned? |
| 20:10 | Finish |