I'm trying to learn ANOVA and see how it applies to a certain experiment. In the experiment we are trying to predict if a certain brain signal is predictive of whether a mouse would do the correct or wrong action. For simplicitly, let us assume that the signal is a scalar real number.

So, there are two classes of interest: correct and mistake. There are also several nuisance parameters:

  • Experiment is performed on multiple mice. There is variance among mice
  • Each mouse performs a task, say, 100 times each day for 10 days. For each mouse there may be variances among days.
  • Mice actually perform one of two different tasks, call them Task1 and Task2. The tasks are very similar, as for both there is a correct and wrong solution. While it is an interesting parameter, for this particular question the task type is a nuisance parameter

So, the goal is to check if the signal is predictive of the classes (Correct/Mistake), excluding the effects of categories Mouse, Day and Task.

After reading on ANOVA, I learned that in case there is one nuisance category, one can perform blocking over that category, and exclude it by means of procedure called Two-Way ANOVA.


  1. If I have multiple nuisance categories, what is a good way to exclude their effect? Should I perform blocking over every combination of the categories?
  2. The number of days and number of tasks per day varies across mice. Is this sort of variability a problem for ANOVA?

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.