diff --git a/R/summarise.r b/R/summarise.r
index fce6df8..e9f1712 100644
--- a/R/summarise.r
+++ b/R/summarise.r
@@ -84,7 +84,7 @@ summarise_.grouped_svy <- function(.data, ..., .dots) {
 #'
 #'
 #' @param .data tbl A \code{tbl_svy} object
-#' @param ... Name-value pairs of summary functions
+#' @param ... Name-value pairs of summarizing expressions, see details
 #' @param .groups Defaults to "drop_last" in srvyr meaning that the last group is peeled
 #' off, but if there are more groups they will be preserved. Other options are "drop", which
 #' drops all groups, "keep" which keeps all of them and "rowwise" which converts the object
@@ -130,6 +130,13 @@ summarise_.grouped_svy <- function(.data, ..., .dots) {
 #'    Calculate an unweighted estimate as you would on a regular \code{tbl_df}.
 #'    Based on dplyr's \code{\link[dplyr]{summarise}}.}
 #'}
+#'
+#' You can use expressions both in the \code{...} of \code{summarize} and also
+#' in the arguments to the summarizing functions. Though this is valid syntactically
+#' it can also allow you to calculate incorrect results (for example if you multiply
+#' the mean by 100, the standard error is also multipled by 100, but the variance
+#' is not).
+#'
 #' @examples
 #' data(api, package = "survey")
 #'
@@ -149,6 +156,17 @@ summarise_.grouped_svy <- function(.data, ..., .dots) {
 #'             api00_mn = survey_mean(api00),
 #'             api_diff = survey_mean(api00 - api99))
 #'
+#' # Expressions are allowed in summarize arguments & inside functions
+#' # Here we can calculate binary variable on the fly and also multiply by 100 to
+#' # get percentages
+#' dstrata %>%
+#'   summarize(api99_over_700_pct = 100 * survey_mean(api99 > 700)
+#'
+#' # But be careful, the variance doesn't scale the same way, so this is wrong!
+#' dstrata %>%
+#'   summarize(api99_over_700_pct = 100 * survey_mean(api99 > 700, vartype = "var")
+#' # Wrong variance!
+#'
 #' @name summarise
 #' @export
 #' @importFrom dplyr summarise
diff --git a/man/summarise.Rd b/man/summarise.Rd
index 44c5008..a9c6ad1 100644
--- a/man/summarise.Rd
+++ b/man/summarise.Rd
@@ -7,7 +7,7 @@
 \arguments{
 \item{.data}{tbl A \code{tbl_svy} object}
 
-\item{...}{Name-value pairs of summary functions}
+\item{...}{Name-value pairs of summarizing expressions, see details}
 
 \item{.groups}{Defaults to "drop_last" in srvyr meaning that the last group is peeled
 off, but if there are more groups they will be preserved. Other options are "drop", which
@@ -58,6 +58,12 @@ The available functions from srvyr are:
    Calculate an unweighted estimate as you would on a regular \code{tbl_df}.
    Based on dplyr's \code{\link[dplyr]{summarise}}.}
 }
+
+You can use expressions both in the \code{...} of \code{summarize} and also
+in the arguments to the summarizing functions. Though this is valid syntactically
+it can also allow you to calculate incorrect results (for example if you multiply
+the mean by 100, the standard error is also multipled by 100, but the variance
+is not).
 }
 \examples{
 data(api, package = "survey")
@@ -78,4 +84,15 @@ dstrata_grp \%>\%
             api00_mn = survey_mean(api00),
             api_diff = survey_mean(api00 - api99))
 
+# Expressions are allowed in summarize arguments & inside functions
+# Here we can calculate binary variable on the fly and also multiply by 100 to
+# get percentages
+dstrata \%>\%
+  summarize(api99_over_700_pct = 100 * survey_mean(api99 > 700)
+
+# But be careful, the variance doesn't scale the same way, so this is wrong!
+dstrata \%>\%
+  summarize(api99_over_700_pct = 100 * survey_mean(api99 > 700, vartype = "var")
+# Wrong variance!
+
 }
diff --git a/vignettes/srvyr-vs-survey.Rmd b/vignettes/srvyr-vs-survey.Rmd
index 088017d..2757ab5 100644
--- a/vignettes/srvyr-vs-survey.Rmd
+++ b/vignettes/srvyr-vs-survey.Rmd
@@ -209,12 +209,6 @@ ggplot(data = out, aes(x = stype, y = api_diff, group = hs_grad_pct, fill = hs_g
 
 ```
 
-
-# CAUTION! Difference in default degrees of freedom
-Though it should be possible to get the exact same results using `srvyr` as you would
-if you used the `survey` package, s
-
-
 # Grab Bag
 
 ## Using `survey` functions on `srvyr` objects
@@ -229,11 +223,49 @@ summary(glm)
 ```
 
 
+## Using expressions to create variables on the fly
+Like `dplyr`, `srvyr` allows you to use expressions in the arguments, 
+allowing you to create variables in a single step. For example, you can
+use expressions:
+
+1) as the arguments inside the survey statistic functions like `survey_mean`
+```{r, message = FALSE}
+strat_design %>%
+  summarize(prop_api99_over_700 = survey_mean(api99 > 700))
+```
+
+2) as an argument to `summarize`
+```{r, message = FALSE}
+strat_design %>%
+  group_by(awards) %>%
+  summarize(percentage = 100 * survey_mean())
+```
+
+3) and you can even create varables inside of `group_by`
+```{r, message = FALSE}
+strat_design %>%
+  group_by(api99_above_700 = api99 > 700) %>%
+  summarize(api00_mn = survey_mean(api00))
+```
+
+Though on-the-fly expressions are syntactically valid, it is possible to make statistically
+invalid numbers from them. For example, though the standard error and confidence intervals 
+can be multiplied by a scalar (like 100), the variance does not scale the same way, so the
+following is invalid:
+
+```{r, message = FALSE}
+# BAD DON'T DO THIS!
+strat_design %>%
+  group_by(awards) %>%
+  summarize(percentage = 100 * survey_mean(vartype = "var"))
+# VARIANCE IS WRONG
+```
+
 ## Non-Standard evaluation
 
 Srvyr supports the non-standard evaluation conventions that dplyr uses.
 If you'd like to use a function programmatically, you can use the functions from 
-rlang like the `{{` operator (aka "curly curly") from `rlang`. 
+rlang like the `{{` operator (aka "curly curly") from `rlang`.
 
 Here's a quick example, but please see the dplyr vignette 
 [`vignette("programming", package = "dplyr")`](https://dplyr.tidyverse.org/articles/programming.html)