So, what do we do about these problems?

And there are several kinds of fixes.

The first basic one is to check the assumptions and look at the data.

What are the most important things that we can look for

and we'll see this again when we talk about robust regression is looking for

outliers and skewed variables that can have strong influences on the data and

take particular attention in neural imaging to behavioral predictors or

clinical and other outcomes in brain behavior correlation or group analysis.

And that's because you might not be able to look at the data on every single brain

One by one, but if you have behavioral predictors,

those are regressed against brain data everywhere in the brain.

So if there's something funny about the distribution of those predictors,

then that's going to influence results all over the brain,

and that's something you can look at and

think about carefully, there are a number of kinds of fixes that one can do.

One family is variable transformation, so for example reaction time is a variable

that's typically highly positively skewed because its bound to zero so

its a positive tails and so it's very typical to take a wild reaction time

to reduce that tails and make it look more normally distributed.