diff --git a/R/class-forecast-quantile.R b/R/class-forecast-quantile.R
index 6d6c1bd3d..b1d1802df 100644
--- a/R/class-forecast-quantile.R
+++ b/R/class-forecast-quantile.R
@@ -96,10 +96,18 @@ is_forecast_quantile <- function(x) {
 }
 
 
-#' @rdname as_forecast_point
+#' @title Convert a `forecast_quantile` to a `forecast_point`
+#'
 #' @description
 #' When converting a `forecast_quantile` object into a `forecast_point` object,
 #' the 0.5 quantile is extracted and returned as the point forecast.
+#'
+#' @param data A `forecast_quantile` object (as created by
+#'   [as_forecast_quantile()]).
+#' @param ... Unused.
+#' @returns A `forecast` object of class `forecast_point`.
+#' @family functions to create forecast objects
+#' @seealso [as_forecast_point()]
 #' @export
 #' @keywords as_forecast
 as_forecast_point.forecast_quantile <- function(data, ...) {
diff --git a/R/class-forecast-sample.R b/R/class-forecast-sample.R
index 5bf50d865..4a0f7906a 100644
--- a/R/class-forecast-sample.R
+++ b/R/class-forecast-sample.R
@@ -72,18 +72,26 @@ is_forecast_sample <- function(x) {
 }
 
 
-#' @rdname as_forecast_quantile
-#' @details # Converting from `forecast_sample` to `forecast_quantile`
+#' @title Convert a `forecast_sample` to a `forecast_quantile`
+#'
+#' @description
 #' When creating a `forecast_quantile` object from a `forecast_sample` object,
-#' the quantiles are estimated by computing empircal quantiles from the samples
+#' the quantiles are estimated by computing empirical quantiles from the samples
 #' via [quantile()]. Note that empirical quantiles are a biased estimator for
 #' the true quantiles in particular in the tails of the distribution and
 #' when the number of available samples is low.
+#'
+#' @param data A `forecast_sample` object (as created by
+#'   [as_forecast_sample()]).
 #' @param probs A numeric vector of quantile levels for which
 #'   quantiles will be computed. Corresponds to the `probs` argument in
 #'   [quantile()].
 #' @param type Type argument passed down to the quantile function. For more
 #'   information, see [quantile()].
+#' @param ... Unused.
+#' @returns A `forecast` object of class `forecast_quantile`.
+#' @family functions to create forecast objects
+#' @seealso [as_forecast_quantile()]
 #' @importFrom stats quantile
 #' @importFrom methods hasArg
 #' @importFrom checkmate assert_numeric
@@ -192,11 +200,20 @@ get_metrics.forecast_sample <- function(x, select = NULL, exclude = NULL, ...) {
 }
 
 
-#' @rdname get_pit_histogram
+#' @title PIT histogram for sample-based forecasts
+#'
+#' @description
+#' Generate a Probability Integral Transformation (PIT) histogram for
+#' sample-based forecast objects. Unlike the quantile-based method
+#' ([get_pit_histogram.forecast_quantile()]), this method supports additional
+#' arguments for handling integer-valued forecasts (`integers` and
+#' `n_replicates`).
+#'
+#' @inherit get_pit_histogram params return
+#' @inheritParams pit_histogram_sample
+#' @seealso [get_pit_histogram()], [pit_histogram_sample()]
 #' @importFrom data.table `:=` as.data.table dcast
 #' @importFrom checkmate assert_int assert_numeric
-#' @inheritParams pit_histogram_sample
-#' @seealso [pit_histogram_sample()]
 #' @export
 get_pit_histogram.forecast_sample <- function(forecast, num_bins = 10,
                                               breaks = NULL, by, integers = c(
diff --git a/R/helper-quantile-interval-range.R b/R/helper-quantile-interval-range.R
index cba7b5ff7..10cc56590 100644
--- a/R/helper-quantile-interval-range.R
+++ b/R/helper-quantile-interval-range.R
@@ -142,6 +142,7 @@ quantile_to_interval_numeric <- function(observed,
 #' based on interval ranges.
 #'
 #' @inheritParams as_forecast_quantile
+#' @inheritParams as_forecast_quantile.forecast_sample
 #' @param keep_quantile_col keep quantile_level column, default is TRUE
 #' @returns A data.table in a long interval interval range format
 #' @importFrom data.table as.data.table
diff --git a/man/as_forecast_binary.Rd b/man/as_forecast_binary.Rd
index 4938c514d..692dda5e7 100644
--- a/man/as_forecast_binary.Rd
+++ b/man/as_forecast_binary.Rd
@@ -113,7 +113,9 @@ Other functions to create forecast objects:
 \code{\link{as_forecast_nominal}()},
 \code{\link{as_forecast_ordinal}()},
 \code{\link{as_forecast_point}()},
+\code{\link{as_forecast_point.forecast_quantile}()},
 \code{\link{as_forecast_quantile}()},
+\code{\link{as_forecast_quantile.forecast_sample}()},
 \code{\link{as_forecast_sample}()}
 }
 \concept{functions to create forecast objects}
diff --git a/man/as_forecast_multivariate_sample.Rd b/man/as_forecast_multivariate_sample.Rd
index 04e8cf690..e647d5748 100644
--- a/man/as_forecast_multivariate_sample.Rd
+++ b/man/as_forecast_multivariate_sample.Rd
@@ -124,7 +124,9 @@ Other functions to create forecast objects:
 \code{\link{as_forecast_nominal}()},
 \code{\link{as_forecast_ordinal}()},
 \code{\link{as_forecast_point}()},
+\code{\link{as_forecast_point.forecast_quantile}()},
 \code{\link{as_forecast_quantile}()},
+\code{\link{as_forecast_quantile.forecast_sample}()},
 \code{\link{as_forecast_sample}()}
 }
 \concept{functions to create forecast objects}
diff --git a/man/as_forecast_nominal.Rd b/man/as_forecast_nominal.Rd
index 1af22c78a..b298ad245 100644
--- a/man/as_forecast_nominal.Rd
+++ b/man/as_forecast_nominal.Rd
@@ -127,7 +127,9 @@ Other functions to create forecast objects:
 \code{\link{as_forecast_multivariate_sample}()},
 \code{\link{as_forecast_ordinal}()},
 \code{\link{as_forecast_point}()},
+\code{\link{as_forecast_point.forecast_quantile}()},
 \code{\link{as_forecast_quantile}()},
+\code{\link{as_forecast_quantile.forecast_sample}()},
 \code{\link{as_forecast_sample}()}
 }
 \concept{functions to create forecast objects}
diff --git a/man/as_forecast_ordinal.Rd b/man/as_forecast_ordinal.Rd
index e6748c011..645ff6db9 100644
--- a/man/as_forecast_ordinal.Rd
+++ b/man/as_forecast_ordinal.Rd
@@ -127,7 +127,9 @@ Other functions to create forecast objects:
 \code{\link{as_forecast_multivariate_sample}()},
 \code{\link{as_forecast_nominal}()},
 \code{\link{as_forecast_point}()},
+\code{\link{as_forecast_point.forecast_quantile}()},
 \code{\link{as_forecast_quantile}()},
+\code{\link{as_forecast_quantile.forecast_sample}()},
 \code{\link{as_forecast_sample}()}
 }
 \concept{functions to create forecast objects}
diff --git a/man/as_forecast_point.Rd b/man/as_forecast_point.Rd
index 83ba83187..9b8855f84 100644
--- a/man/as_forecast_point.Rd
+++ b/man/as_forecast_point.Rd
@@ -1,10 +1,8 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/class-forecast-point.R,
-%   R/class-forecast-quantile.R
+% Please edit documentation in R/class-forecast-point.R
 \name{as_forecast_point}
 \alias{as_forecast_point}
 \alias{as_forecast_point.default}
-\alias{as_forecast_point.forecast_quantile}
 \title{Create a \code{forecast} object for point forecasts}
 \usage{
 as_forecast_point(data, ...)
@@ -16,8 +14,6 @@ as_forecast_point(data, ...)
   predicted = NULL,
   ...
 )
-
-\method{as_forecast_point}{forecast_quantile}(data, ...)
 }
 \arguments{
 \item{data}{A data.frame (or similar) with predicted and observed values.
@@ -43,8 +39,16 @@ predicted values. This column will be renamed to "predicted".}
 A \code{forecast} object of class \code{forecast_point}
 }
 \description{
-When converting a \code{forecast_quantile} object into a \code{forecast_point} object,
-the 0.5 quantile is extracted and returned as the point forecast.
+Process and validate a data.frame (or similar) or similar with forecasts
+and observations. If the input passes all input checks, those functions will
+be converted to a \code{forecast} object. A forecast object is a \code{data.table} with
+a class \code{forecast} and an additional class that depends on the forecast type.
+
+The arguments \code{observed}, \code{predicted}, etc. make it possible to rename
+existing columns of the input data to match the required columns for a
+forecast object. Using the argument \code{forecast_unit}, you can specify
+the columns that uniquely identify a single forecast (and thereby removing
+other, unneeded columns. See section "Forecast Unit" below for details).
 }
 \section{Target format}{
 The input for all further scoring needs to be a data.frame or similar with
@@ -67,7 +71,9 @@ Other functions to create forecast objects:
 \code{\link{as_forecast_multivariate_sample}()},
 \code{\link{as_forecast_nominal}()},
 \code{\link{as_forecast_ordinal}()},
+\code{\link{as_forecast_point.forecast_quantile}()},
 \code{\link{as_forecast_quantile}()},
+\code{\link{as_forecast_quantile.forecast_sample}()},
 \code{\link{as_forecast_sample}()}
 }
 \concept{functions to create forecast objects}
diff --git a/man/as_forecast_point.forecast_quantile.Rd b/man/as_forecast_point.forecast_quantile.Rd
new file mode 100644
index 000000000..460fd53d8
--- /dev/null
+++ b/man/as_forecast_point.forecast_quantile.Rd
@@ -0,0 +1,36 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/class-forecast-quantile.R
+\name{as_forecast_point.forecast_quantile}
+\alias{as_forecast_point.forecast_quantile}
+\title{Convert a \code{forecast_quantile} to a \code{forecast_point}}
+\usage{
+\method{as_forecast_point}{forecast_quantile}(data, ...)
+}
+\arguments{
+\item{data}{A \code{forecast_quantile} object (as created by
+\code{\link[=as_forecast_quantile]{as_forecast_quantile()}}).}
+
+\item{...}{Unused.}
+}
+\value{
+A \code{forecast} object of class \code{forecast_point}.
+}
+\description{
+When converting a \code{forecast_quantile} object into a \code{forecast_point} object,
+the 0.5 quantile is extracted and returned as the point forecast.
+}
+\seealso{
+\code{\link[=as_forecast_point]{as_forecast_point()}}
+
+Other functions to create forecast objects: 
+\code{\link{as_forecast_binary}()},
+\code{\link{as_forecast_multivariate_sample}()},
+\code{\link{as_forecast_nominal}()},
+\code{\link{as_forecast_ordinal}()},
+\code{\link{as_forecast_point}()},
+\code{\link{as_forecast_quantile}()},
+\code{\link{as_forecast_quantile.forecast_sample}()},
+\code{\link{as_forecast_sample}()}
+}
+\concept{functions to create forecast objects}
+\keyword{as_forecast}
diff --git a/man/as_forecast_quantile.Rd b/man/as_forecast_quantile.Rd
index 4cf407d0e..052125269 100644
--- a/man/as_forecast_quantile.Rd
+++ b/man/as_forecast_quantile.Rd
@@ -1,10 +1,8 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/class-forecast-quantile.R,
-%   R/class-forecast-sample.R
+% Please edit documentation in R/class-forecast-quantile.R
 \name{as_forecast_quantile}
 \alias{as_forecast_quantile}
 \alias{as_forecast_quantile.default}
-\alias{as_forecast_quantile.forecast_sample}
 \title{Create a \code{forecast} object for quantile-based forecasts}
 \usage{
 as_forecast_quantile(data, ...)
@@ -17,13 +15,6 @@ as_forecast_quantile(data, ...)
   quantile_level = NULL,
   ...
 )
-
-\method{as_forecast_quantile}{forecast_sample}(
-  data,
-  probs = c(0.05, 0.25, 0.5, 0.75, 0.95),
-  type = 7,
-  ...
-)
 }
 \arguments{
 \item{data}{A data.frame (or similar) with predicted and observed values.
@@ -48,13 +39,6 @@ predicted values. This column will be renamed to "predicted".}
 \item{quantile_level}{(optional) Name of the column in \code{data} that contains
 the quantile level of the predicted values. This column will be renamed to
 "quantile_level". Only applicable to quantile-based forecasts.}
-
-\item{probs}{A numeric vector of quantile levels for which
-quantiles will be computed. Corresponds to the \code{probs} argument in
-\code{\link[=quantile]{quantile()}}.}
-
-\item{type}{Type argument passed down to the quantile function. For more
-information, see \code{\link[=quantile]{quantile()}}.}
 }
 \value{
 A \code{forecast} object of class \code{forecast_quantile}
@@ -90,14 +74,6 @@ strictly necessary.
 See the \link{example_quantile} data set for an example.
 }
 
-\section{Converting from \code{forecast_sample} to \code{forecast_quantile}}{
-When creating a \code{forecast_quantile} object from a \code{forecast_sample} object,
-the quantiles are estimated by computing empircal quantiles from the samples
-via \code{\link[=quantile]{quantile()}}. Note that empirical quantiles are a biased estimator for
-the true quantiles in particular in the tails of the distribution and
-when the number of available samples is low.
-}
-
 \section{Forecast unit}{
 In order to score forecasts, \code{scoringutils} needs to know which of the rows
 of the data belong together and jointly form a single forecast. This is
@@ -142,6 +118,8 @@ Other functions to create forecast objects:
 \code{\link{as_forecast_nominal}()},
 \code{\link{as_forecast_ordinal}()},
 \code{\link{as_forecast_point}()},
+\code{\link{as_forecast_point.forecast_quantile}()},
+\code{\link{as_forecast_quantile.forecast_sample}()},
 \code{\link{as_forecast_sample}()}
 }
 \concept{functions to create forecast objects}
diff --git a/man/as_forecast_quantile.forecast_sample.Rd b/man/as_forecast_quantile.forecast_sample.Rd
new file mode 100644
index 000000000..0a83baa5b
--- /dev/null
+++ b/man/as_forecast_quantile.forecast_sample.Rd
@@ -0,0 +1,50 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/class-forecast-sample.R
+\name{as_forecast_quantile.forecast_sample}
+\alias{as_forecast_quantile.forecast_sample}
+\title{Convert a \code{forecast_sample} to a \code{forecast_quantile}}
+\usage{
+\method{as_forecast_quantile}{forecast_sample}(
+  data,
+  probs = c(0.05, 0.25, 0.5, 0.75, 0.95),
+  type = 7,
+  ...
+)
+}
+\arguments{
+\item{data}{A \code{forecast_sample} object (as created by
+\code{\link[=as_forecast_sample]{as_forecast_sample()}}).}
+
+\item{probs}{A numeric vector of quantile levels for which
+quantiles will be computed. Corresponds to the \code{probs} argument in
+\code{\link[=quantile]{quantile()}}.}
+
+\item{type}{Type argument passed down to the quantile function. For more
+information, see \code{\link[=quantile]{quantile()}}.}
+
+\item{...}{Unused.}
+}
+\value{
+A \code{forecast} object of class \code{forecast_quantile}.
+}
+\description{
+When creating a \code{forecast_quantile} object from a \code{forecast_sample} object,
+the quantiles are estimated by computing empirical quantiles from the samples
+via \code{\link[=quantile]{quantile()}}. Note that empirical quantiles are a biased estimator for
+the true quantiles in particular in the tails of the distribution and
+when the number of available samples is low.
+}
+\seealso{
+\code{\link[=as_forecast_quantile]{as_forecast_quantile()}}
+
+Other functions to create forecast objects: 
+\code{\link{as_forecast_binary}()},
+\code{\link{as_forecast_multivariate_sample}()},
+\code{\link{as_forecast_nominal}()},
+\code{\link{as_forecast_ordinal}()},
+\code{\link{as_forecast_point}()},
+\code{\link{as_forecast_point.forecast_quantile}()},
+\code{\link{as_forecast_quantile}()},
+\code{\link{as_forecast_sample}()}
+}
+\concept{functions to create forecast objects}
diff --git a/man/as_forecast_sample.Rd b/man/as_forecast_sample.Rd
index bf179530c..534ec460c 100644
--- a/man/as_forecast_sample.Rd
+++ b/man/as_forecast_sample.Rd
@@ -109,7 +109,9 @@ Other functions to create forecast objects:
 \code{\link{as_forecast_nominal}()},
 \code{\link{as_forecast_ordinal}()},
 \code{\link{as_forecast_point}()},
-\code{\link{as_forecast_quantile}()}
+\code{\link{as_forecast_point.forecast_quantile}()},
+\code{\link{as_forecast_quantile}()},
+\code{\link{as_forecast_quantile.forecast_sample}()}
 }
 \concept{functions to create forecast objects}
 \keyword{as_forecast}
diff --git a/man/get_pit_histogram.Rd b/man/get_pit_histogram.Rd
index aded199ba..b59847796 100644
--- a/man/get_pit_histogram.Rd
+++ b/man/get_pit_histogram.Rd
@@ -1,25 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/class-forecast-quantile.R,
-%   R/class-forecast-sample.R, R/get-pit-histogram.R
+%   R/get-pit-histogram.R
 \name{get_pit_histogram.forecast_quantile}
 \alias{get_pit_histogram.forecast_quantile}
-\alias{get_pit_histogram.forecast_sample}
 \alias{get_pit_histogram}
 \alias{get_pit_histogram.default}
 \title{Probability integral transformation histogram}
 \usage{
 \method{get_pit_histogram}{forecast_quantile}(forecast, num_bins = NULL, breaks = NULL, by, ...)
 
-\method{get_pit_histogram}{forecast_sample}(
-  forecast,
-  num_bins = 10,
-  breaks = NULL,
-  by,
-  integers = c("nonrandom", "random", "ignore"),
-  n_replicates = NULL,
-  ...
-)
-
 get_pit_histogram(forecast, num_bins, breaks, by, ...)
 
 \method{get_pit_histogram}{default}(forecast, num_bins, breaks, by, ...)
@@ -51,16 +40,6 @@ every model and location, specify \code{by = c("model", "location")}.}
 functions via \code{...}. See the \emph{Customising metrics} section below for
 details on how to use \code{\link[purrr:partial]{purrr::partial()}} to pass arguments to individual
 metrics.}
-
-\item{integers}{How to handle integer forecasts (count data). This is based
-on methods described Czado et al. (2007). If "nonrandom" (default) the
-function will use the non-randomised PIT method. If "random", will use the
-randomised PIT method. If "ignore", will treat integer forecasts as if they
-were continuous.}
-
-\item{n_replicates}{The number of draws for the randomised PIT for discrete
-predictions. Will be ignored if forecasts are continuous or \code{integers} is
-not set to \code{random}.}
 }
 \value{
 A data.table with density values for each bin in the PIT histogram.
@@ -93,7 +72,4 @@ Rosalind M. Eggo, W. John Edmunds (2019) Assessing the performance of
 real-time epidemic forecasts: A case study of Ebola in the Western Area
 region of Sierra Leone, 2014-15, \doi{10.1371/journal.pcbi.1006785}
 }
-\seealso{
-\code{\link[=pit_histogram_sample]{pit_histogram_sample()}}
-}
 \keyword{scoring}
diff --git a/man/get_pit_histogram.forecast_sample.Rd b/man/get_pit_histogram.forecast_sample.Rd
new file mode 100644
index 000000000..372388937
--- /dev/null
+++ b/man/get_pit_histogram.forecast_sample.Rd
@@ -0,0 +1,67 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/class-forecast-sample.R
+\name{get_pit_histogram.forecast_sample}
+\alias{get_pit_histogram.forecast_sample}
+\title{PIT histogram for sample-based forecasts}
+\usage{
+\method{get_pit_histogram}{forecast_sample}(
+  forecast,
+  num_bins = 10,
+  breaks = NULL,
+  by,
+  integers = c("nonrandom", "random", "ignore"),
+  n_replicates = NULL,
+  ...
+)
+}
+\arguments{
+\item{forecast}{A forecast object (a validated data.table with predicted and
+observed values).}
+
+\item{num_bins}{The number of bins in the PIT histogram. For sample-based
+forecasts, the default is 10 bins. For quantile-based forecasts, the
+default is one bin for each available quantile.
+You can control the number of bins by supplying a number. This is fine for
+sample-based pit histograms, but may fail for quantile-based formats. In
+this case it is preferred to supply explicit breaks points using the
+\code{breaks} argument.}
+
+\item{breaks}{Numeric vector with the break points for the bins in the
+PIT histogram. This is preferred when creating a PIT histogram based on
+quantile-based data. Default is \code{NULL} and breaks will be determined by
+\code{num_bins}. If \code{breaks} is used, \code{num_bins} will be ignored.
+0 and 1 will always be added as left and right bounds, respectively.}
+
+\item{by}{Character vector with the columns according to which the
+PIT values shall be grouped. If you e.g. have the columns 'model' and
+'location' in the input data and want to have a PIT histogram for
+every model and location, specify \code{by = c("model", "location")}.}
+
+\item{integers}{How to handle integer forecasts (count data). This is based
+on methods described Czado et al. (2007). If "nonrandom" (default) the
+function will use the non-randomised PIT method. If "random", will use the
+randomised PIT method. If "ignore", will treat integer forecasts as if they
+were continuous.}
+
+\item{n_replicates}{The number of draws for the randomised PIT for discrete
+predictions. Will be ignored if forecasts are continuous or \code{integers} is
+not set to \code{random}.}
+
+\item{...}{Currently unused. You \emph{cannot} pass additional arguments to scoring
+functions via \code{...}. See the \emph{Customising metrics} section below for
+details on how to use \code{\link[purrr:partial]{purrr::partial()}} to pass arguments to individual
+metrics.}
+}
+\value{
+A data.table with density values for each bin in the PIT histogram.
+}
+\description{
+Generate a Probability Integral Transformation (PIT) histogram for
+sample-based forecast objects. Unlike the quantile-based method
+(\code{\link[=get_pit_histogram.forecast_quantile]{get_pit_histogram.forecast_quantile()}}), this method supports additional
+arguments for handling integer-valued forecasts (\code{integers} and
+\code{n_replicates}).
+}
+\seealso{
+\code{\link[=get_pit_histogram]{get_pit_histogram()}}, \code{\link[=pit_histogram_sample]{pit_histogram_sample()}}
+}
diff --git a/tests/testthat/test-class-forecast-point.R b/tests/testthat/test-class-forecast-point.R
index fb2fd161d..666d1dc50 100644
--- a/tests/testthat/test-class-forecast-point.R
+++ b/tests/testthat/test-class-forecast-point.R
@@ -8,6 +8,15 @@ test_that("as_forecast_point() works", {
   )
 })
 
+test_that("as_forecast_point.forecast_quantile() extracts median without extra args", {
+  quantile_forecast <- as_forecast_quantile(na.omit(example_quantile))
+  result <- as_forecast_point(quantile_forecast)
+  expect_true(is_forecast_point(result))
+  expect_false("quantile_level" %in% colnames(result))
+  expect_false("sample_id" %in% colnames(result))
+  expect_no_condition(as_forecast_point(quantile_forecast))
+})
+
 
 # ==============================================================================
 # is_forecast_point() # nolint: commented_code_linter
diff --git a/tests/testthat/test-class-forecast-quantile.R b/tests/testthat/test-class-forecast-quantile.R
index 4f46a5ed3..be8229b70 100644
--- a/tests/testthat/test-class-forecast-quantile.R
+++ b/tests/testthat/test-class-forecast-quantile.R
@@ -93,6 +93,28 @@ test_that("as_forecast_quantile() works with a data.frame", {
   expect_no_condition(as_forecast_quantile(example_quantile_df))
 })
 
+test_that("as_forecast_quantile.forecast_sample() accepts probs and type args", {
+  result <- as_forecast_quantile(
+    example_sample_continuous,
+    probs = c(0.1, 0.5, 0.9),
+    type = 7
+  )
+  expect_true(is_forecast_quantile(result))
+  expect_equal(sort(unique(result$quantile_level)), c(0.1, 0.5, 0.9))
+  expect_no_condition(
+    as_forecast_quantile(na.omit(example_sample_continuous), probs = c(0.1, 0.5, 0.9))
+  )
+})
+
+test_that("formals differ between as_forecast_quantile methods", {
+  sample_formals <- names(formals(scoringutils:::as_forecast_quantile.forecast_sample))
+  default_formals <- names(formals(scoringutils:::as_forecast_quantile.default))
+  expect_true("probs" %in% sample_formals)
+  expect_false("probs" %in% default_formals)
+  expect_true("quantile_level" %in% default_formals)
+  expect_false("quantile_level" %in% sample_formals)
+})
+
 test_that("as_forecast_quantiles works", {
   samples <- data.frame(
     date = as.Date("2020-01-01") + 1:10,
@@ -427,3 +449,9 @@ test_that("get_pit_histogram.forecast_quantile() works as expected", {
   # check printing works
   expect_output(print(pit_quantile))
 })
+
+test_that("get_pit_histogram.forecast_quantile() does not accept integers arg", {
+  expect_false(
+    "integers" %in% names(formals(scoringutils:::get_pit_histogram.forecast_quantile))
+  )
+})
diff --git a/tests/testthat/test-class-forecast-sample.R b/tests/testthat/test-class-forecast-sample.R
index 995e49106..9c8ef8807 100644
--- a/tests/testthat/test-class-forecast-sample.R
+++ b/tests/testthat/test-class-forecast-sample.R
@@ -74,3 +74,14 @@ test_that("get_pit_histogram.forecast_sample() works as expected", {
   expect_s3_class(pit_continuous, c("data.table", "data.frame"), exact = TRUE)
   expect_s3_class(pit_integer, c("data.table", "data.frame"), exact = TRUE)
 })
+
+test_that("get_pit_histogram.forecast_sample() accepts integers and n_replicates args", {
+  result <- get_pit_histogram(
+    example_sample_discrete,
+    by = "model",
+    integers = "random",
+    n_replicates = 50
+  )
+  expect_named(result, c("model", "density", "bin", "mid"))
+  expect_true(all(result$density >= 0))
+})