diff --git a/R/p_significance.R b/R/p_significance.R
index dc3d02091..78d2c2287 100644
--- a/R/p_significance.R
+++ b/R/p_significance.R
@@ -151,8 +151,16 @@ p_significance.lm <- function(x, threshold = "default", ci = 0.95, verbose = TRU
   out <- result$out
   posterior <- result$posterior
 
-  # calculate the ROPE range
-  if (all(threshold == "default")) {
+  # calculate the ROPE range - for multiple thresholds, we have to check
+  # each list element for "default", to replace it with the appropriate range
+  if (is.list(threshold)) {
+    threshold <- lapply(threshold, function(i) {
+      if (all(i == "default")) {
+        i <- bayestestR::rope_range(x, verbose = verbose)
+      }
+      i
+    })
+  } else if (all(threshold == "default")) {
     threshold <- bayestestR::rope_range(x, verbose = verbose)
   }
 
diff --git a/tests/testthat/test-p_significance.R b/tests/testthat/test-p_significance.R
index 16c71f4f2..d40305d29 100644
--- a/tests/testthat/test-p_significance.R
+++ b/tests/testthat/test-p_significance.R
@@ -1,33 +1,38 @@
 skip_on_cran()
-skip_if_not_installed("bayestestR", minimum_version = "0.14.1")
+skip_if_not_installed("bayestestR", minimum_version = "0.14.0.10")
+skip_if_not_installed("distributional")
+skip_if_not_installed("withr")
 
-test_that("p_significance", {
-  data(mtcars)
-  m <- lm(mpg ~ gear + wt + cyl + hp, data = mtcars)
-  set.seed(123)
-  x <- p_significance(m)
-  expect_identical(c(nrow(x), ncol(x)), c(5L, 5L))
-  expect_named(x, c("Parameter", "CI", "CI_low", "CI_high", "ps"))
-  expect_snapshot(print(x))
+withr::with_environment(
+  new.env(),
+  test_that("p_significance", {
+    data(mtcars)
+    m <<- lm(mpg ~ gear + wt + cyl + hp, data = mtcars)
+    set.seed(123)
+    x <- p_significance(m)
+    expect_identical(c(nrow(x), ncol(x)), c(5L, 5L))
+    expect_named(x, c("Parameter", "CI", "CI_low", "CI_high", "ps"))
+    expect_snapshot(print(x))
 
-  mp <- model_parameters(m)
-  set.seed(123)
-  x2 <- p_significance(mp)
-  expect_equal(x$ps, x2$ps, tolerance = 1e-4)
+    mp <- model_parameters(m)
+    set.seed(123)
+    x2 <- p_significance(mp)
+    expect_equal(x$ps, x2$ps, tolerance = 1e-4)
 
-  set.seed(123)
-  x <- p_significance(m, ci = 0.8)
-  expect_equal(x$ps, c(1, 0.3983, 0.9959, 0.6188, 0), tolerance = 1e-3)
+    set.seed(123)
+    x <- p_significance(m, ci = 0.8)
+    expect_equal(x$ps, c(1, 0.3983, 0.9959, 0.6188, 0), tolerance = 1e-3)
 
-  set.seed(123)
-  x <- p_significance(m, threshold = 0.5)
-  expect_equal(x$ps, c(1, 0.4393, 0.9969, 0.6803, 0), tolerance = 1e-4)
+    set.seed(123)
+    x <- p_significance(m, threshold = 0.5)
+    expect_equal(x$ps, c(1, 0.4393, 0.9969, 0.6803, 0), tolerance = 1e-4)
 
-  set.seed(123)
-  # Test p_significance with custom thresholds for specific parameters
-  x <- p_significance(m, threshold = list(cyl = 0.5, wt = 0.7))
-  expect_equal(x$ps, c(1, 0.6002, 0.995, 0.6805, 0), tolerance = 1e-4)
-})
+    set.seed(123)
+    # Test p_significance with custom thresholds for specific parameters
+    x <- p_significance(m, threshold = list(cyl = 0.5, wt = 0.7))
+    expect_equal(x$ps, c(1, 0.5982, 0.9955, 0.6803, 1e-04), tolerance = 1e-4)
+  })
+)
 
 test_that("p_significance, glmmTMB", {
   skip_if_not_installed("glmmTMB")