diff --git a/NEWS.md b/NEWS.md
index a4fd3df0f..0cc49b785 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -23,7 +23,8 @@
 * `performance_score()` should no longer fail for models where scoring rules
   can't be calculated. Instead, an informative message is returned.
 
-* `check_outliers()` now properly accept the `percentage_central` argument when using the `"mcd"` method.
+* `check_outliers()` now properly accept the `percentage_central` argument when
+  using the `"mcd"` method.
 
 # performance 0.10.8
 
diff --git a/R/check_outliers.R b/R/check_outliers.R
index e01b20e12..ba8b6418d 100644
--- a/R/check_outliers.R
+++ b/R/check_outliers.R
@@ -25,7 +25,8 @@
 #' @param ID Optional, to report an ID column along with the row number.
 #' @param ... When `method = "ics"`, further arguments in `...` are passed
 #' down to [ICSOutlier::ics.outlier()]. When `method = "mahalanobis"`,
-#' they are  passed down to [stats::mahalanobis()].
+#' they are  passed down to [stats::mahalanobis()]. `percentage_central` can
+#' be specified when `method = "mcd"`.
 #'
 #' @return A logical vector of the detected outliers with a nice printing
 #'   method: a check (message) on whether outliers were detected or not. The
@@ -388,16 +389,16 @@ check_outliers.default <- function(x,
   )
 
   # Get data
-  data <- insight::get_data(x, verbose = FALSE)
+  my_data <- insight::get_data(x, verbose = FALSE)
 
   # Remove non-numerics
-  data <- datawizard::data_select(data, select = is.numeric)
+  my_data <- datawizard::data_select(my_data, select = is.numeric)
 
   # Thresholds
   if (is.null(threshold)) {
-    thresholds <- .check_outliers_thresholds(data)
+    thresholds <- .check_outliers_thresholds(my_data)
   } else if (is.list(threshold)) {
-    thresholds <- .check_outliers_thresholds(data)
+    thresholds <- .check_outliers_thresholds(my_data)
     thresholds[names(threshold)] <- threshold[names(threshold)]
   } else {
     insight::format_error(
@@ -414,15 +415,15 @@ check_outliers.default <- function(x,
 
   # Others
   if (all(method %in% c("cook", "pareto"))) {
-    df <- data.frame(Row = seq_len(nrow(as.data.frame(data))))
+    my_df <- data.frame(Row = seq_len(nrow(as.data.frame(my_data))))
     outlier_count <- list()
     outlier_var <- list()
   } else {
-    out <- check_outliers(data, method, threshold)
+    out <- check_outliers(my_data, method, threshold)
     outlier_var <- attributes(out)$outlier_var
     outlier_count <- attributes(out)$outlier_count
-    df <- attributes(out)$data
-    df <- df[!names(df) == "Outlier"]
+    my_df <- attributes(out)$data
+    my_df <- my_df[names(my_df) != "Outlier"]
   }
 
   # Cook
@@ -432,7 +433,7 @@ check_outliers.default <- function(x,
       threshold = thresholds$cook
     )$data_cook
 
-    df <- datawizard::data_merge(list(df, data_cook),
+    my_df <- datawizard::data_merge(list(my_df, data_cook),
       join = "full",
       by = "Row"
     )
@@ -462,7 +463,7 @@ check_outliers.default <- function(x,
       )
     }
   } else {
-    method <- method[!(method == "cook")]
+    method <- method[method != "cook"]
   }
 
   # Pareto
@@ -472,7 +473,7 @@ check_outliers.default <- function(x,
       threshold = thresholds$pareto
     )$data_pareto
 
-    df <- datawizard::data_merge(list(df, data_pareto),
+    my_df <- datawizard::data_merge(list(my_df, data_pareto),
       join = "full",
       by = "Row"
     )
@@ -502,7 +503,7 @@ check_outliers.default <- function(x,
       )
     }
   } else {
-    method <- method[!(method == "pareto")]
+    method <- method[method != "pareto"]
   }
 
   outlier_count$all <- datawizard::convert_na_to(outlier_count$all,
@@ -534,21 +535,21 @@ check_outliers.default <- function(x,
   thresholds <- thresholds[names(thresholds) %in% method]
 
   # Composite outlier score
-  df$Outlier <- rowMeans(df[grepl("Outlier_", names(df), fixed = TRUE)])
-  df <- df[c(names(df)[names(df) != "Outlier"], "Outlier")]
+  my_df$Outlier <- rowMeans(my_df[grepl("Outlier_", names(my_df), fixed = TRUE)])
+  my_df <- my_df[c(names(my_df)[names(my_df) != "Outlier"], "Outlier")]
 
   # Out
-  outlier <- df$Outlier > 0.5
+  outlier <- my_df$Outlier > 0.5
 
   # Attributes
   class(outlier) <- c("check_outliers", "see_check_outliers", class(outlier))
-  attr(outlier, "data") <- df
+  attr(outlier, "data") <- my_df
   attr(outlier, "threshold") <- thresholds
   attr(outlier, "method") <- method
   attr(outlier, "text_size") <- 3
   attr(outlier, "influential_obs") <- .influential_obs(x)
   attr(outlier, "variables") <- "(Whole model)"
-  attr(outlier, "raw_data") <- data
+  attr(outlier, "raw_data") <- my_data
   attr(outlier, "outlier_var") <- outlier_var
   attr(outlier, "outlier_count") <- outlier_count
 
@@ -801,7 +802,7 @@ check_outliers.data.frame <- function(x,
   )
 
   # Remove non-numerics
-  data <- x
+  my_data <- x
   x <- x[, vapply(x, is.numeric, logical(1)), drop = FALSE]
 
   # Check args
@@ -844,20 +845,20 @@ check_outliers.data.frame <- function(x,
   outlier_var <- out.meta$outlier_var
 
   # Combine outlier data
-  df <- out[vapply(out, is.data.frame, logical(1))]
-  if (length(df) > 1 && !is.null(ID)) {
-    df <- datawizard::data_merge(df, by = c("Row", ID))
-  } else if (length(df) > 1) {
-    df <- datawizard::data_merge(df, by = "Row")
+  my_df <- out[vapply(out, is.data.frame, logical(1))]
+  if (length(my_df) > 1 && !is.null(ID)) {
+    my_df <- datawizard::data_merge(my_df, by = c("Row", ID))
+  } else if (length(my_df) > 1) {
+    my_df <- datawizard::data_merge(my_df, by = "Row")
   } else {
-    df <- df[[1]]
+    my_df <- my_df[[1]]
   }
 
   # Composite outlier score
-  df$Outlier <- rowMeans(df[grepl("Outlier_", names(df), fixed = TRUE)])
+  my_df$Outlier <- rowMeans(my_df[grepl("Outlier_", names(my_df), fixed = TRUE)])
 
   # Out
-  outlier <- df$Outlier > 0.5
+  outlier <- my_df$Outlier > 0.5
 
   # Combine outlier frequency table
   if (length(outlier_count) > 1 && !is.null(ID)) {
@@ -899,12 +900,12 @@ check_outliers.data.frame <- function(x,
 
   # Attributes
   class(outlier) <- c("check_outliers", "see_check_outliers", class(outlier))
-  attr(outlier, "data") <- df
+  attr(outlier, "data") <- my_df
   attr(outlier, "threshold") <- thresholds
   attr(outlier, "method") <- method
   attr(outlier, "text_size") <- 3
   attr(outlier, "variables") <- names(x)
-  attr(outlier, "raw_data") <- data
+  attr(outlier, "raw_data") <- my_data
   attr(outlier, "outlier_var") <- outlier_var
   attr(outlier, "outlier_count") <- outlier_count
   outlier
@@ -917,7 +918,7 @@ check_outliers.data.frame <- function(x,
     outlier.list <- lapply(outlier.list, function(x) {
       x[x[[Outlier_method]] >= 0.5, ]
     })
-    outlier.list <- outlier.list[lapply(outlier.list, nrow) > 0]
+    outlier.list <- outlier.list[vapply(outlier.list, nrow, numeric(1)) > 0]
     outlier.list <- lapply(outlier.list, datawizard::data_remove,
       Outlier_method,
       as_data_frame = TRUE
@@ -1220,7 +1221,7 @@ check_outliers.grouped_df <- function(x,
   }
 
   # Initialize elements
-  data <- data.frame()
+  my_data <- data.frame()
   out <- NULL
   thresholds <- list()
   outlier_var <- list()
@@ -1229,24 +1230,24 @@ check_outliers.grouped_df <- function(x,
   # Loop through groups
   for (i in seq_along(grps)) {
     rows <- grps[[i]]
-    subset <- check_outliers(
+    outliers_subset <- check_outliers(
       as.data.frame(x[rows, ]),
       method = method,
       threshold = threshold,
       ID = ID,
       ...
     )
-    data <- rbind(data, as.data.frame(subset))
-    out <- c(out, subset)
-    thresholds[[paste0("group_", i)]] <- attributes(subset)$threshold
+    my_data <- rbind(my_data, as.data.frame(outliers_subset))
+    out <- c(out, outliers_subset)
+    thresholds[[paste0("group_", i)]] <- attributes(outliers_subset)$threshold
     outlier_var[[i]] <- lapply(
-      attributes(subset)$outlier_var, lapply, function(y) {
+      attributes(outliers_subset)$outlier_var, lapply, function(y) {
         y$Row <- rows[which(seq_along(rows) %in% y$Row)]
         y
       }
     )
     outlier_count[[i]] <- lapply(
-      attributes(subset)$outlier_count, function(y) {
+      attributes(outliers_subset)$outlier_count, function(y) {
         y$Row <- rows[which(seq_along(rows) %in% y$Row)]
         y
       }
@@ -1267,16 +1268,16 @@ check_outliers.grouped_df <- function(x,
     info$groups$.rows[[x]] <- as.data.frame(info$groups$.rows[[x]])
   })
 
-  data[names(info$groups)[1]] <- do.call(rbind, groups)
-  data <- datawizard::data_relocate(
-    data,
+  my_data[names(info$groups)[1]] <- do.call(rbind, groups)
+  my_data <- datawizard::data_relocate(
+    my_data,
     select = names(info$groups)[1],
     after = "Row"
   )
-  data$Row <- seq_len(nrow(data))
+  my_data$Row <- seq_len(nrow(my_data))
 
   class(out) <- c("check_outliers", "see_check_outliers", class(out))
-  attr(out, "data") <- data
+  attr(out, "data") <- my_data
   attr(out, "method") <- method
   attr(out, "threshold") <- thresholds[[1]]
   attr(out, "text_size") <- 3
@@ -1437,7 +1438,7 @@ check_outliers.metabin <- check_outliers.metagen
   bci <- 1 - 0.001
   cook <- stats::qf(0.5, ncol(x), nrow(x) - ncol(x))
   pareto <- 0.7
-  mahalanobis <- stats::qchisq(p = 1 - 0.001, df = ncol(x))
+  mahalanobis_value <- stats::qchisq(p = 1 - 0.001, df = ncol(x))
   mahalanobis_robust <- stats::qchisq(p = 1 - 0.001, df = ncol(x))
   mcd <- stats::qchisq(p = 1 - 0.001, df = ncol(x))
   ics <- 0.001
@@ -1454,7 +1455,7 @@ check_outliers.metabin <- check_outliers.metagen
     bci = bci,
     cook = cook,
     pareto = pareto,
-    mahalanobis = mahalanobis,
+    mahalanobis = mahalanobis_value,
     mahalanobis_robust = mahalanobis_robust,
     mcd = mcd,
     ics = ics,
@@ -1730,7 +1731,8 @@ check_outliers.metabin <- check_outliers.metagen
 .check_outliers_mcd <- function(x,
                                 threshold = stats::qchisq(p = 1 - 0.001, df = ncol(x)),
                                 percentage_central = 0.75,
-                                ID.names = NULL) {
+                                ID.names = NULL,
+                                ...) {
   out <- data.frame(Row = seq_len(nrow(x)))
 
   if (!is.null(ID.names)) {
diff --git a/tests/testthat/test-check_outliers.R b/tests/testthat/test-check_outliers.R
index e464028d0..7d5585b27 100644
--- a/tests/testthat/test-check_outliers.R
+++ b/tests/testthat/test-check_outliers.R
@@ -90,6 +90,10 @@ test_that("mcd which", {
     tail(which(check_outliers(mtcars[1:4], method = "mcd", threshold = 45))),
     31L
   )
+  out <- check_outliers(mtcars, method = "mcd")
+  expect_identical(sum(out), 8)
+  out <- check_outliers(mtcars, method = "mcd", percentage_central = 0.5)
+  expect_identical(sum(out), 15)
 })
 
 ## FIXME: Fails on CRAN/windows
@@ -197,10 +201,10 @@ test_that("all methods which", {
         "mahalanobis", "mahalanobis_robust", "mcd", "optics", "lof"
       ),
       threshold = list(
-        "zscore" = 2.2, "zscore_robust" = 2.2, "iqr" = 1.2,
-        "ci" = 0.95, "eti" = 0.95, "hdi" = 0.90, "bci" = 0.95,
-        "mahalanobis" = 20, "mahalanobis_robust" = 25, "mcd" = 25,
-        "optics" = 14, "lof" = 0.005
+        zscore = 2.2, zscore_robust = 2.2, iqr = 1.2,
+        ci = 0.95, eti = 0.95, hdi = 0.90, bci = 0.95,
+        mahalanobis = 20, mahalanobis_robust = 25, mcd = 25,
+        optics = 14, lof = 0.005
       )
     )),
     as.integer(c(9, 15, 16, 19, 20, 28, 29, 31))
@@ -219,10 +223,10 @@ test_that("multiple methods with ID", {
       "mahalanobis", "mahalanobis_robust", "mcd", "optics", "lof"
     ),
     threshold = list(
-      "zscore" = 2.2, "zscore_robust" = 2.2, "iqr" = 1.2,
-      "ci" = 0.95, "eti" = 0.95, "hdi" = 0.90, "bci" = 0.95,
-      "mahalanobis" = 20, "mahalanobis_robust" = 25, "mcd" = 25,
-      "optics" = 14, "lof" = 0.005
+      zscore = 2.2, zscore_robust = 2.2, iqr = 1.2,
+      ci = 0.95, eti = 0.95, hdi = 0.90, bci = 0.95,
+      mahalanobis = 20, mahalanobis_robust = 25, mcd = 25,
+      optics = 14, lof = 0.005
     ),
     ID = "car"
   ))