Update randomness (#909)

* concepts.wip exercises changed to deprecated

* Revert "concepts.wip exercises changed to deprecated"

This reverts commit 28f91a8.

* Change annelyns-infiltration slug for old version

* Revert "Change annelyns-infiltration slug for old version"

This reverts commit 09dfe5d.

* propagates edits from captains-log

---------

Co-authored-by: Colin Leach <[email protected]>

Author: colinleach

concepts/randomness/about.md

@@ -27,7 +27,7 @@ What this function does depends on the arguments you give it.
 There are _many_ options.
 
 With no arguments, it generates a float between 0 and 1.
-This is a [`uniform`][uniform-distribution] with all values equally likely, as discussed in the Working with Distributions section, below.
+This is a [`uniform`][uniform] distribution with all values equally likely, as discussed in the Working with Distributions section, below.
 
 A single integer argument generates a vector of that length.
 
@@ -81,6 +81,18 @@ julia> rand(1:10, 5)
 In the `rand(1:10, 5)` example above, notice that there are (coincidentally) repeating values, because each pick is independent.
 This is "sampling with replacement", discussed in more detail below.
 
+For floating-point values in a range, you will usually need to specify a step size.
+Otherwise the step will default to 1.0, which is rarely useful.
+
+```julia-repl
+julia> rand(2.4:0.01:3.2, 4)
+4-element Vector{Float64}:
+ 3.19
+ 2.53
+ 3.14
+ 3.13
+```
+
 Alternatively, supply an array or tuple, and `rand` will return a random entry:
 
 ```julia-repl
@@ -290,7 +302,7 @@ For a much wider range of options, there is the [`Distributions.jl`][distributio
 [sampling-with-replacement]: https://www.youtube.com/watch?v=LnGFL_A6A6A
 [standard-deviation]: https://simple.wikipedia.org/wiki/Standard_deviation
 [truly-random]: https://www.malwarebytes.com/blog/news/2013/09/in-computers-are-random-numbers-really-random
-[uniform-distribution]: https://www.investopedia.com/terms/u/uniform-distribution.asp#:~:text=In%20statistics%2C%20uniform%20distribution%20refers,a%20spade%20is%20equally%20likely.
+[uniform]: https://www.investopedia.com/terms/u/uniform-distribution.asp#:~:text=In%20statistics%2C%20uniform%20distribution%20refers,a%20spade%20is%20equally%20likely.
 [reproducibility]: https://docs.julialang.org/en/v1/stdlib/Random/#Reproducibility
 [statsbase]: https://juliastats.org/StatsBase.jl/stable/
 [sbsample]: https://juliastats.org/StatsBase.jl/stable/sampling/#StatsBase.sample

concepts/randomness/introduction.md

@@ -27,7 +27,7 @@ What this function does depends on the arguments you give it.
 There are _many_ options.
 
 With no arguments, it generates a float between 0 and 1.
-This is a `uniform` with all values equally likely, as discussed in the Working with Distributions section, below.
+This is a `uniform` distribution with all values equally likely, as discussed in the Working with Distributions section, below.
 
 A single integer argument generates a vector of that length.
 
@@ -81,6 +81,18 @@ julia> rand(1:10, 5)
 In the `rand(1:10, 5)` example above, notice that there are (coincidentally) repeating values, because each pick is independent.
 This is "sampling with replacement", discussed in more detail below.
 
+For floating-point values in a range, you will usually need to specify a step size.
+Otherwise the step will default to 1.0, which is rarely useful.
+
+```julia-repl
+julia> rand(2.4:0.01:3.2, 4)
+4-element Vector{Float64}:
+ 3.19
+ 2.53
+ 3.14
+ 3.13
+```
+
 Alternatively, supply an array or tuple, and `rand` will return a random entry:
 
 ```julia-repl