Apply suggestions from code review

Co-authored-by: Ivan Pribec <ivan.pribec@gmail.com>

Author: jvdp1

doc/specs/stdlib_stats.md

@@ -188,9 +188,9 @@ Experimental
 
 Returns the median of all the elements of `array`, or of the elements of `array` along dimension `dim` if provided, and if the corresponding element in `mask` is `true`.
 
-After that the elements are sorted in an increasing order, e.g. `array_sorted =
-sort(array)`, the median of the elements of `array` is defined as the "middle"
-element. If `n = size(array)` is an even number, the median is:
+The median of the elements of `array` is defined as the "middle"
+element, after that the elements are sorted in an increasing order, e.g. `array_sorted =
+sort(array)`. If `n = size(array)` is an even number, the median is:
 
 ```
 median(array) = array_sorted( floor( (n + 1) / 2.))

src/stdlib_stats_median.fypp

@@ -11,7 +11,7 @@ submodule (stdlib_stats) stdlib_stats_median
   ! Use "ord_sort" rather than "sort" because the former can be much faster for arrays
   ! that are already partly sorted. While it is slightly slower for random arrays,
   ! ord_sort seems a better overall choice.
-  use stdlib_sorting, only:sort => ord_sort
+  use stdlib_sorting, only: sort => ord_sort
   implicit none
 
 contains

src/tests/stats/test_median.fypp

@@ -5,7 +5,7 @@ program test_median
     use stdlib_kinds, only: sp, dp, qp, &
         int8, int16, int32, int64
     use stdlib_stats, only: median, mean
-    use,intrinsic :: ieee_arithmetic, only : ieee_is_nan
+    use, intrinsic :: ieee_arithmetic, only : ieee_is_nan
     implicit none
     real(sp), parameter :: sptol = 1000 * epsilon(1._sp)
     real(dp), parameter :: dptol = 2000 * epsilon(1._dp)
@@ -112,7 +112,7 @@ subroutine test_median_${k1}$()
     call check(size(d0) == 0, 'test_median_${k1}$: should be of size 0')
 
     d1 = [10., 2., -3., -4., 6., -6., 7., -8., 9., 0., 1., 20.]
-    call check(mod(size(d1), 2) == 0, 'stest_median_${k1}$: hould be an even number')
+    call check(mod(size(d1), 2) == 0, 'test_median_${k1}$: should be an even number')
 
     d2 = reshape(d1, [3, 4])
     call check(mod(size(d2), 2) == 0, 'test_median_${k1}$: should be an even number')