Another example is Word in experiments with verbal materials.
So let's say you're studying the effect of positive and negative words.
And you only choose one positive word which is puppies and
one negative word which is murder.
And you do a scan where you compare puppies and murder.
Well they differ in positive versus negative, but
they also differ in many other features as well.
So, you might treat, you might first include a poll population of words,
many kinds of positive and negative words.
And then you might want to model a word as a random effect, so
that you can generalize to unobserved words as well,
drawing from the population of negative or positive words.
One of the key points is in a mixed effects model,
we choose whether to model each effect as fixed or random.
So here are the implications of that choice.