Q-Q plots are graphical tools to determine whether the assumption that the data came from a specific parametric distribution is plausible. They are generated by **plotting the theoretical and actual percentiles against each other in a scatter plot**. If the points fall on the diagonal straight line, it is reasonable to assume that the data came from that type of distribution.

The FEM (fixed effect model) assumes a chi-squared distribution, and ExpressAnalyst supports a Q-Q plot to check the validity of this assumption. The data will rarely fall perfectly on the straight line, even when they are randomly sampled from a known distribution, so you should look for large deviations such as the Q-Q plot below. In this case, since there is a significant deviation, the REM (random effect model) may be more appropriate since it does not assume any parametric distribution.