Parametric (normal data) tests are more powerful than non-parametric
So, you should use parametric tests if you can
The simplest and best way of doing this is by looking
Is my data normal?
Is my data normal?
Parametric (normal data) tests are more powerful than non-parametric
So, you should use parametric tests if you can
The simplest and best way of doing this is by looking
A slight wrinkle, its not that the data is normal but rather that the residuals are.
A slight detour: Residuals
What to do if your data isn't normal?
Transform it
Use non-parametric tests
Transforming data
Applying a mathematical function to make the data/residuals fit a normal distribution
What! Surely thats dodgy?
Is converting feet into metres?
You are just changing the scale on which the data is measured.
Lots of transformations, but we'll look at log
Log transformation
A log-transformation stretches out the left hand side (smaller values) of the distribution and squashes in the right hand side (larger values). This is obviously useful where the data set has a long tail to the right (right skewed)
Log transformation
Non-parametric tests
Usually based on ranks
Why is that less powerful?
Think about 5,10,1000
That becomes 1,2,3
Next Week
The wait is over, lets do some statistical tests including