Here we 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.
|09:00||An introduction to the Statistics in reproducibility module||
Who is this module for ?
How can I get some help if I get stuck on solving for an exercise or a question ?
How can I validate this module ?
|09:00||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
Notion of model comparison : BIC/Akaike
Notion of bayesian statistics
|09:00||Effect size and variation of effect sizes in brain imaging||
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
|14:00||P-values and their issues||
What is a p-value ?
What should I be aware of when I see a ‘significant’ p-value ?
|14:00||About statistical power||
What is power
Why is it important: issues with low statistical power
Some tools for power for neuroimaging
|17:00||The positive Predictive Value||What is the positive Predictive Value (PPV) ?|
|17:30||Cultural and psychological issues||What have we learned?|