In a famous experiment, a group of volunteers are asked to toss a fair coin 100 times and note down the results of each toss (heads, H, or tails, T). It is generally easy to spot the participants who fake the results by writing down what they think is a random sequence of Hs and Ts instead of actually tossing the coin because they tend not to include as many "streaks'' of repeated results as would be expected by chance.
If they had access to a Python interpreter, here's how they could a more plausibly random set of results:
In [x]: res = ['H', 'T']
In [37]: tosses = ''.join([res[i] for i in np.random.randint(2, size=100)])
In [38]: tosses
Out[38]: 'TTHHTHHTTHHHTHTTHHHTHHTHTTHHTHHTTTTHHHHHHHHTTTHTTHHHHHHHTHHHTHHHH
THTTTHTTHHHHTHTTTTHTTTHTHHTTHHHHHHH'
This virtual experiment features a run of 8 heads in a row, and two runs of 7 heads in a row:
TAILS | i | HEADS
---------------------------
| 8 | *
| 7 | **
| 6 |
| 5 |
** | 4 | **
*** | 3 | ***
******* | 2 | ******
********** | 1 | ********
(This figure was produced by the following code.)
import numpy as np
toss_results = ['H', 'T']
tosses = ''.join([toss_results[i] for i in np.random.randint(2, size=100)])
head_seq = [len(s) for s in tosses.split('T') if s]
tail_seq = [len(s) for s in tosses.split('H') if s]
max_streak_len = max(max(head_seq), max(tail_seq))
head_seq_counts = [head_seq.count(i) for i in range(1,max_streak_len+1)]
tail_seq_counts = [tail_seq.count(i) for i in range(1,max_streak_len+1)]
max_streak_count = max(max(head_seq_counts), max(tail_seq_counts))
print(tosses)
print('{t:^{flen}} | i | {h:^{flen}}'.format(t='TAILS', h='HEADS',
flen=max_streak_count))
print('-'*(max_streak_count*2+7))
for i in range(max_streak_len,0,-1):
print('{tstreak:>{flen}s} |{i:^3d}| {hstreak:<{flen}s}'.format(
tstreak='*'*tail_seq_counts[i-1], hstreak='*'*head_seq_counts[i-1],
flen=max_streak_count, i=i))