diff --git a/R/kruskal_wallis_test.R b/R/kruskal_wallis_test.R
index dd0eebc..aca3e98 100644
--- a/R/kruskal_wallis_test.R
+++ b/R/kruskal_wallis_test.R
@@ -2,19 +2,15 @@
 #' @name kruskal_wallis_test
 #' @description This function performs a Kruskal-Wallis rank sum test, to test
 #' the null hypothesis that the population median of all of the groups are
-#' equal. The alternative is that they differ in at least one. If `paired = TRUE`,
-#' a paired Friedman test is conducted.
+#' equal. The alternative is that they differ in at least one.
 #'
 #' @inheritParams mann_whitney_test
-#' @param paired Logical, if `TRUE`, a paired Friedman test is conducted (see
-#' [`friedman.test()`]).
 #'
 #' @return A data frame with test results.
 #'
 #' @details The function simply is a wrapper around [`kruskal.test()`]. The
 #' weighted version of the Kruskal-Wallis test is based on the `survey` package,
-#' using [`survey::svyranktest()`]. When `paired = TRUE`, a paired Friedman test
-#' is conducted (see [`friedman.test()`]).
+#' using [`survey::svyranktest()`].
 #'
 #' @examples
 #' data(efc)
@@ -24,13 +20,25 @@
 kruskal_wallis_test <- function(data,
                                 select = NULL,
                                 by = NULL,
-                                weights = NULL,
-                                paired = FALSE) {
+                                weights = NULL) {
   insight::check_if_installed("datawizard")
 
   # sanity checks
   .sanitize_htest_input(data, select, by, weights)
 
+  # does select indicate more than one variable?
+  if (length(select) > 1) {
+    if (!is.null(by)) {
+      insight::format_error("If `select` specifies more than one variable, `by` must be `NULL`.")
+    }
+    # we convert the data into long format, and create a grouping variable
+    data <- datawizard::data_to_long(data[select], names_to = "group", values_to = "scale")
+    by <- select[2]
+    select <- select[1]
+    # after converting to long, we have the "grouping" variable first in the data
+    colnames(data) <- c(by, select)
+  }
+
   # get data
   dv <- data[[select]]
   grp <- data[[by]]
@@ -43,9 +51,9 @@ kruskal_wallis_test <- function(data,
     insight::format_error("At least two groups are required, i.e. data must have at least two unique levels in `by` for `kruskal_wallis_test()`.") # nolint
   }
   if (is.null(weights)) {
-    .calculate_kw(dv, grp, paired)
+    .calculate_kw(dv, grp)
   } else {
-    .calculate_weighted_kw(dv, grp, data[[weights]], paired = TRUE)
+    .calculate_weighted_kw(dv, grp, data[[weights]])
   }
 }
 
@@ -101,10 +109,7 @@ kruskal_wallis_test <- function(data,
   }, numeric(1))
 
   if (paired) {
-    tab <- as.table(round(stats::xtabs(x[[3]] ~ x[[1]] + x[[2]])))
-    class(tab) <- "table"
-    # perfom friedman test for paired data
-    result <- stats::friedman.test(tab)
+    ## TODO: paired no working. should call `friedman.test()`
   } else {
     design <- survey::svydesign(ids = ~0, data = dat, weights = ~w)
     result <- survey::svyranktest(formula = x ~ g, design, test = "KruskalWallis")
diff --git a/R/mann_whitney_test.R b/R/mann_whitney_test.R
index d900bf2..1caf823 100644
--- a/R/mann_whitney_test.R
+++ b/R/mann_whitney_test.R
@@ -9,7 +9,10 @@
 #'
 #' @param data A data frame.
 #' @param select Name of the dependent variable (as string) to be used for the
-#' test.
+#' test. `select` can also be a character vector, specifing the names of
+#' multiple continuous variables. In this case, `by` is ignored and variables
+#' specified in `select` are used to compute the test. This can be useful if
+#' the data is in wide-format and no grouping variable is available.
 #' @param by Name of the grouping variable to be used for the test. If `by` is
 #' not a factor, it will be coerced to a factor. For `chi_squared_test()`, if
 #' `probabilities` is provided, `by` must be `NULL`.
@@ -52,6 +55,23 @@ mann_whitney_test <- function(data,
   # sanity checks
   .sanitize_htest_input(data, select, by, weights)
 
+  # does select indicate more than one variable?
+  if (length(select) > 1) {
+    # sanity check - may only specify two variable names
+    if (length(select) > 2) {
+      insight::format_error("You may only specify two variables for Mann-Whitney test.")
+    }
+    if (!is.null(by)) {
+      insight::format_error("If `select` specifies more than one variable, `by` must be `NULL`.")
+    }
+    # we convert the data into long format, and create a grouping variable
+    data <- datawizard::data_to_long(data[select], names_to = "group", values_to = "scale")
+    by <- select[2]
+    select <- select[1]
+    # after converting to long, we have the "grouping" variable first in the data
+    colnames(data) <- c(by, select)
+  }
+
   # get data
   dv <- data[[select]]
   grp <- data[[by]]
@@ -61,7 +81,7 @@ mann_whitney_test <- function(data,
 
   # only two groups allowed
   if (insight::n_unique(grp) > 2) {
-    insight::format_error("Only two groups are allowed for Mann-Whitney-Test. Please use `kruskal_wallis_test()` for more than two groups.") # nolint
+    insight::format_error("Only two groups are allowed for Mann-Whitney test. Please use `kruskal_wallis_test()` for more than two groups.") # nolint
   }
 
   # value labels
@@ -267,21 +287,22 @@ mann_whitney_test <- function(data,
   }
 
   # check if arguments have correct length (length of 1)
-  if (length(select) != 1 || !is.character(select)) {
-    insight::format_error("Argument `select` must be the name of a single variable.")
+  if (!is.character(select)) {
+    insight::format_error("Argument `select` must be a character string with the name(s) of the variable(s).")
   }
   if (length(by) != 1 || !is.character(by)) {
-    insight::format_error("Argument `by` must be the name of a single variable.")
+    insight::format_error("Argument `by` must be a character string with the name of a single variable.")
   }
   if (!is.null(weights) && length(weights) != 1) {
-    insight::format_error("Argument `weights` must be the name of a single variable.")
+    insight::format_error("Argument `weights` must be a character string with the name of a single variable.")
   }
 
   # check if "select" is in data
-  if (!select %in% colnames(data)) {
+  if (!all(select %in% colnames(data))) {
+    not_found <- setdiff(select, colnames(data))[1]
     insight::format_error(
-      sprintf("Variable '%s' not found in data frame.", select),
-      .misspelled_string(colnames(data), select, "Maybe misspelled?")
+      sprintf("Variable '%s' not found in data frame.", not_found),
+      .misspelled_string(colnames(data), not_found, "Maybe misspelled?")
     )
   }
   # check if "by" is in data
diff --git a/man/chi_squared_test.Rd b/man/chi_squared_test.Rd
index 9bcfd73..71a0b15 100644
--- a/man/chi_squared_test.Rd
+++ b/man/chi_squared_test.Rd
@@ -18,7 +18,10 @@ chi_squared_test(
 \item{data}{A data frame.}
 
 \item{select}{Name of the dependent variable (as string) to be used for the
-test.}
+test. \code{select} can also be a character vector, specifing the names of
+multiple continuous variables. In this case, \code{by} is ignored and variables
+specified in \code{select} are used to compute the test. This can be useful if
+the data is in wide-format and no grouping variable is available.}
 
 \item{by}{Name of the grouping variable to be used for the test. If \code{by} is
 not a factor, it will be coerced to a factor. For \code{chi_squared_test()}, if
diff --git a/man/kruskal_wallis_test.Rd b/man/kruskal_wallis_test.Rd
index 0b30814..6b8bf3f 100644
--- a/man/kruskal_wallis_test.Rd
+++ b/man/kruskal_wallis_test.Rd
@@ -4,28 +4,22 @@
 \alias{kruskal_wallis_test}
 \title{Kruskal-Wallis test}
 \usage{
-kruskal_wallis_test(
-  data,
-  select = NULL,
-  by = NULL,
-  weights = NULL,
-  paired = FALSE
-)
+kruskal_wallis_test(data, select = NULL, by = NULL, weights = NULL)
 }
 \arguments{
 \item{data}{A data frame.}
 
 \item{select}{Name of the dependent variable (as string) to be used for the
-test.}
+test. \code{select} can also be a character vector, specifing the names of
+multiple continuous variables. In this case, \code{by} is ignored and variables
+specified in \code{select} are used to compute the test. This can be useful if
+the data is in wide-format and no grouping variable is available.}
 
 \item{by}{Name of the grouping variable to be used for the test. If \code{by} is
 not a factor, it will be coerced to a factor. For \code{chi_squared_test()}, if
 \code{probabilities} is provided, \code{by} must be \code{NULL}.}
 
 \item{weights}{Name of an (optional) weighting variable to be used for the test.}
-
-\item{paired}{Logical, if \code{TRUE}, a paired Friedman test is conducted (see
-\code{\link[=friedman.test]{friedman.test()}}).}
 }
 \value{
 A data frame with test results.
@@ -33,14 +27,12 @@ A data frame with test results.
 \description{
 This function performs a Kruskal-Wallis rank sum test, to test
 the null hypothesis that the population median of all of the groups are
-equal. The alternative is that they differ in at least one. If \code{paired = TRUE},
-a paired Friedman test is conducted.
+equal. The alternative is that they differ in at least one.
 }
 \details{
 The function simply is a wrapper around \code{\link[=kruskal.test]{kruskal.test()}}. The
 weighted version of the Kruskal-Wallis test is based on the \code{survey} package,
-using \code{\link[survey:svyranktest]{survey::svyranktest()}}. When \code{paired = TRUE}, a paired Friedman test
-is conducted (see \code{\link[=friedman.test]{friedman.test()}}).
+using \code{\link[survey:svyranktest]{survey::svyranktest()}}.
 }
 \examples{
 data(efc)
diff --git a/man/mann_whitney_test.Rd b/man/mann_whitney_test.Rd
index 1401a04..9ab30e6 100644
--- a/man/mann_whitney_test.Rd
+++ b/man/mann_whitney_test.Rd
@@ -16,7 +16,10 @@ mann_whitney_test(
 \item{data}{A data frame.}
 
 \item{select}{Name of the dependent variable (as string) to be used for the
-test.}
+test. \code{select} can also be a character vector, specifing the names of
+multiple continuous variables. In this case, \code{by} is ignored and variables
+specified in \code{select} are used to compute the test. This can be useful if
+the data is in wide-format and no grouping variable is available.}
 
 \item{by}{Name of the grouping variable to be used for the test. If \code{by} is
 not a factor, it will be coerced to a factor. For \code{chi_squared_test()}, if