diff --git a/R/hdi.R b/R/hdi.R
index 39f2a9ac7..4fccb8898 100644
--- a/R/hdi.R
+++ b/R/hdi.R
@@ -382,7 +382,7 @@ hdi.get_predicted <- function(x, ...) {
 
   if (window_size < 2) {
     if (verbose) {
-      insight::format_message("`ci` is too small or x does not contain enough data points, returning NAs.")
+      insight::format_alert("`ci` is too small or x does not contain enough data points, returning NAs.")
     }
     return(data.frame(
       "CI" = ci,
@@ -395,7 +395,7 @@ hdi.get_predicted <- function(x, ...) {
 
   if (nCIs < 1) {
     if (verbose) {
-      insight::format_message("`ci` is too large or x does not contain enough data points, returning NAs.")
+      insight::format_alert("`ci` is too large or x does not contain enough data points, returning NAs.")
     }
     return(data.frame(
       "CI" = ci,
@@ -413,7 +413,7 @@ hdi.get_predicted <- function(x, ...) {
   if (n_candies > 1) {
     if (any(diff(sort(min_i)) != 1)) {
       if (verbose) {
-        insight::format_message("Identical densities found along different segments of the distribution, choosing rightmost.")
+        insight::format_alert("Identical densities found along different segments of the distribution, choosing rightmost.")
       }
       min_i <- max(min_i)
     } else {
diff --git a/R/print.R b/R/print.R
index 589b5c5a5..fbe3a2fd6 100644
--- a/R/print.R
+++ b/R/print.R
@@ -266,6 +266,7 @@ print.bayesfactor_parameters <- function(x, digits = 3, log = FALSE, ...) {
       cat(paste0(colnames(formatted_table), ": "))
     }
     cat(formatted_table[1, 1])
+    cat("\n")
   } else {
     # print for data frame
 
diff --git a/tests/testthat/test-hdi.R b/tests/testthat/test-hdi.R
index a649a9d62..d74ac27ca 100644
--- a/tests/testthat/test-hdi.R
+++ b/tests/testthat/test-hdi.R
@@ -6,18 +6,17 @@ test_that("hdi", {
   skip_if_not_or_load_if_installed("httr")
   skip_if_not_or_load_if_installed("BayesFactor")
 
-  expect_equal(hdi(distribution_normal(1000), ci = .90)$CI_low[1], -1.64, tolerance = 0.02)
-  expect_equal(nrow(hdi(distribution_normal(1000), ci = c(.80, .90, .95))), 3, tolerance = 0.01)
+  expect_equal(hdi(distribution_normal(1000), ci = 0.90)$CI_low[1], -1.64, tolerance = 0.02)
+  expect_equal(nrow(hdi(distribution_normal(1000), ci = c(0.80, 0.90, 0.95))), 3, tolerance = 0.01)
   expect_equal(hdi(distribution_normal(1000), ci = 1)$CI_low[1], -3.29, tolerance = 0.02)
-  expect_equal(nchar(capture.output(print(hdi(distribution_normal(1000))))), 22)
-  expect_equal(length(capture.output(print(hdi(distribution_normal(1000), ci = c(.80, .90))))), 5)
+  expect_identical(nchar(capture.output(print(hdi(distribution_normal(1000))))), 22L)
+  expect_length(capture.output(print(hdi(distribution_normal(1000), ci = c(0.80, 0.90)))), 5)
 
-
-  expect_warning(hdi(c(2, 3, NA)))
-  expect_warning(hdi(c(2, 3)))
-  expect_warning(hdi(distribution_normal(1000), ci = 0.0000001))
-  expect_warning(hdi(distribution_normal(1000), ci = 950))
-  expect_warning(hdi(c(0, 0, 0)))
+  expect_message(hdi(c(2, 3, NA)))
+  expect_message(hdi(c(2, 3)))
+  expect_message(hdi(distribution_normal(1000), ci = 0.0000001))
+  expect_message(hdi(distribution_normal(1000), ci = 950))
+  expect_message(hdi(c(0, 0, 0)))
 })
 
 
@@ -65,7 +64,7 @@ test_that("ci - BayesFactor", {
   skip_if_not_or_load_if_installed("httr")
   skip_if_not_or_load_if_installed("BayesFactor")
 
-  mod_bf <- proportionBF(y = 15, N = 25, p = .5)
+  mod_bf <- proportionBF(y = 15, N = 25, p = 0.5)
   p_bf <- insight::get_parameters(mod_bf)
 
   expect_equal(