I am not convinced. A statistical test for assessing statistical fairness is chi-square:

Well yes. Chi² is the "standard test" in certain circles; but it needs

1. to be implemented properly;
2. to include Yates' (of Fisher-Yates fame) correction;
3. to be used correctly;

I haven't investigated the particular implementation you used, but I did install it so I could run your code. A few times...

C:\test>junk123
best_shuffle : There's a >25% chance, and a <50% chance, that this dat
+a is random.
good_shuffle : There's a >1% chance, and a <5% chance, that this data 
+is random.
bad_shuffle : There's a <1% chance that this data is random.

C:\test>junk123
best_shuffle : There's a >75% chance, and a <90% chance, that this dat
+a is random.
good_shuffle : There's a >25% chance, and a <50% chance, that this dat
+a is random.
bad_shuffle : There's a >50% chance, and a <75% chance, that this data
+ is random.

C:\test>junk123
best_shuffle : There's a >10% chance, and a <25% chance, that this dat
+a is random.
good_shuffle : There's a >5% chance, and a <10% chance, that this data
+ is random.
bad_shuffle : There's a >50% chance, and a <75% chance, that this data
+ is random.
[download]

In 3 runs, it evaluates the probability that the results from (one of) the best PRNGs available, is actually random, varies between 10% and 90%, whilst that of a deliberately buggered PRNG is between 1% and 75%.

Honestly, those 3 sets of results look as good an approximation of random as I can think of.

I'll investigate whether it is the way you are using it; the implementation, or the potted interpretations it is producing later, once I've woken properly; but at first glance, I'd have to say "somethings not right".

(May be we could feed S::CS its own results and see it it judges itself random :) )