It’s not so clear. Here’s a better test that shows that something is perhaps not perfect:
10000 Iterations: result Average= #(1.0001, 0.99764, 0.996538, 1.00153, 0.999011, 1.00366, 1.00299, 1.0017, 0.998557, 0.998278)
Average sum= 10.0
Items Greater than 1.25= #(2458, 2322, 2308, 2274, 2126, 2174, 2093, 2160, 2264, 2279)
Items between 1.0 and 1.25= #(2553, 2640, 2620, 2742, 2858, 2838, 2995, 2848, 2719, 2730)
Items between 0.75 and 1.0= #(2483, 2654, 2688, 2808, 2874, 2916, 2835, 2888, 2765, 2711)
Items Lesser than 0.75= #(2506, 2384, 2384, 2176, 2142, 2072, 2077, 2104, 2252, 2280)
OK
OK
10000 Iterations: result Average= #(1.00219, 1.00148, 1.00133, 0.997843, 0.997292, 1.00525, 0.997232, 0.999076, 0.998237, 1.00006)
Average sum= 9.99999
Items Greater than 1.25= #(2556, 2356, 2266, 2186, 2104, 2196, 2061, 2090, 2249, 2259)
Items between 1.0 and 1.25= #(2424, 2645, 2790, 2767, 2829, 2907, 2926, 2860, 2688, 2682)
Items between 0.75 and 1.0= #(2526, 2721, 2677, 2818, 2877, 2811, 2882, 2961, 2822, 2848)
Items Lesser than 0.75= #(2494, 2278, 2267, 2229, 2190, 2086, 2131, 2089, 2241, 2211)
OK
OK
10000 Iterations: result Average= #(0.996662, 0.997425, 1.00213, 0.996569, 1.00407, 1.00292, 0.998174, 1.00067, 1.0021, 0.999291)
Average sum= 10.0
Items Greater than 1.25= #(2480, 2264, 2278, 2206, 2190, 2114, 2095, 2065, 2300, 2213)
Items between 1.0 and 1.25= #(2487, 2689, 2778, 2708, 2851, 2935, 2906, 3011, 2715, 2790)
Items between 0.75 and 1.0= #(2469, 2704, 2732, 2810, 2865, 2915, 2844, 2899, 2738, 2781)
Items Lesser than 0.75= #(2564, 2343, 2212, 2276, 2094, 2036, 2155, 2025, 2247, 2216)
OK
OK
50000 Iterations: result Average= #(0.999116, 0.999397, 0.9974, 1.00102, 1.00134, 0.999913, 0.999637, 1.00331, 0.998206, 1.00066)
Average sum= 10.050000 Iterations: result Average= #(0.998922, 1.00049, 0.998887, 1.00087, 1.0001, 0.999614, 1.00071, 0.999402, 1.00061, 1.00039)
Average sum= 10.0
Items Greater than 1.25= #(12440, 11860, 11348, 11059, 10711, 10449, 10429, 10527, 11320, 11319)
Items between 1.0 and 1.25= #(12426, 13125, 13568, 13999, 14227, 14512, 14759, 14387, 13838, 13677)
Items between 0.75 and 1.0= #(12625, 13315, 13568, 13916, 14397, 14535, 14440, 14552, 13506, 13604)
Items Lesser than 0.75= #(12509, 11700, 11516, 11026, 10665, 10504, 10372, 10534, 11336, 11400)
OK
OK
50000 Iterations: result Average= #(0.999465, 0.999724, 1.00083, 1.00032, 0.99808, 0.998528, 0.999047, 1.00075, 1.00219, 1.00109)
Average sum= 10.0
Items Greater than 1.25= #(12563, 11714, 11375, 11145, 10525, 10465, 10260, 10478, 11435, 11342)
Items between 1.0 and 1.25= #(12433, 13232, 13573, 13893, 14331, 14554, 14743, 14566, 13748, 13712)
Items between 0.75 and 1.0= #(12415, 13252, 13789, 13796, 14321, 14381, 14496, 14512, 13639, 13704)
Items Lesser than 0.75= #(12589, 11802, 11263, 11166, 10823, 10600, 10501, 10444, 11178, 11242)
OK
OK
There’s always a similar behavior:
- We found more numbers greater than 1.25 at the beginning
- We found more numbers lesser than 0.75 at the beginning
- We found less numbers between 0.75 and 1.25 at the beginning
I don’t know if this behavior is something obliged by the problem conditions or not.
Not fantastic:
Distribution