diff --git a/R/rescale_weights.R b/R/rescale_weights.R
index de5d1874e..61d2f2738 100644
--- a/R/rescale_weights.R
+++ b/R/rescale_weights.R
@@ -68,6 +68,12 @@
 #'   divided by the squared mean of the weights. The scales sample weights are
 #'   then divided by the design effect.
 #'
+#'   Some tests on real-world survey-data suggest that, in comparison to the
+#'   Carle-method, the Kish-method comes closer to estimates from a regular
+#'   survey-design using the **survey** package. Note that these tests are not
+#'   representative and it is recommended to check your results against a
+#'   standard survey-design.
+#'
 #' @references
 #'   - Asparouhov T. (2006). General Multi-Level Modeling with Sampling
 #'   Weights. Communications in Statistics - Theory and Methods 35: 439-460
diff --git a/man/rescale_weights.Rd b/man/rescale_weights.Rd
index 65b4ec6cd..907f7314d 100644
--- a/man/rescale_weights.Rd
+++ b/man/rescale_weights.Rd
@@ -85,6 +85,12 @@ which means the sum of all weights equals the sample size. Next, the design
 effect (\emph{Kish 1965}) is calculated, which is the mean of the squared weights
 divided by the squared mean of the weights. The scales sample weights are
 then divided by the design effect.
+
+Some tests on real-world survey-data suggest that, in comparison to the
+Carle-method, the Kish-method comes closer to estimates from a regular
+survey-design using the \strong{survey} package. Note that these tests are not
+representative and it is recommended to check your results against a
+standard survey-design.
 }
 }
 \examples{