When should I specify REML = FALSE in lmer()?

Question

I am running a basic Mixed Effect Model with lmer(), in R. Let say I have 2 within-subject conditions. In each condition the subject provides one measure.

lmer(measure ~ condition + (1|subject),
      REML = TRUE, data = My_data)

The argument REML is TRUE by default. Yet, in several examples I read, people set it as FALSE.

According to the documentations "logical scalar - Should the estimates be chosen to optimize the REML criterion (as opposed to the log-likelihood)?"

Are there real differences in the estimates of fixed or random effects when I use one method or another. When should REML be TRUE? When should it be FALSE?

Answer 1

Based on the comment above, a good answer can be found here:

https://stats.stackexchange.com/questions/272633/how-to-decide-whether-to-set-reml-to-true-or-false/272654#272654

To cite the author: "It's generally good to use REML, if it is available, when you are interested in the magnitude of the random effects variances, but never when you are comparing models with different fixed effects via hypothesis tests or information-theoretic criteria such as AIC."