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Number Buffet
ko

처음 1,000개의 정규분포를 따르는 수

-1.5544, 0.0929, -2.0042, -0.1948, 0.0092, -0.2858, -0.5520, 0.6205, -0.3037, -0.2576, -0.0586, 0.2810, -0.1841, 0.0970, 0.5389, -0.1473, -2.3349, 0.9118, -0.5039, -0.4622, 0.7363, -1.2305, 0.3360, -0.0160, 0.4424, 0.6366, 1.3528, -1.0821, -0.2941, 0.2322, -0.9358, 0.3435, 2.2647, -1.1814, -0.6401, 0.8638, -1.4141, 1.2517, -0.6194, 0.5495, 0.1093, 0.4490, 0.7460, 0.5181, -0.7512, -0.1976, 0.8679, -0.8928, 0.3566, 0.5879, -1.1012, 0.1503, 0.0558, 0.1697, -0.9147, 0.5898, 1.8092, -0.9823, -1.9892, -1.8641, 1.1496, 0.1162, 1.2656, -0.6282, 0.0079, 0.7121, -0.3091, 1.9971, -1.3092, -0.5735, 0.3698, 0.2574, -1.4204, -1.8355, -2.3185, -1.5133, -0.6620, -0.3070, -0.2509, -0.9381, 2.0731, -1.5373, -0.7029, -0.7740, -0.0122, -0.6580, -0.6122, -0.0336, -1.3017, 0.0735, 1.6390, 0.6890, -0.5200, -0.4487, 0.2087, 0.3402, -1.4661, 0.1020, 0.1711, -0.3281, 1.0617, 1.4287, 1.5392, 0.3186, 1.5202, -0.0326, -0.1735, -0.3404, -1.4086, 0.3481, 1.5157, 0.7770, -0.9417, 1.5894, -1.4699, 0.1985, -1.2916, -0.5854, -1.0773, -0.3245, -0.0161, 0.4242, 0.3004, 0.4774, -0.7928, 0.2363, 1.6275, 1.0561, -0.6351, 1.1408, -0.8967, 0.5092, 0.2485, -0.1887, -0.4369, 1.2757, -0.1130, -0.2910, -1.0593, -0.7217, 1.4687, -0.1655, 0.3707, 1.9257, -1.1297, 0.1360, -0.3535, 0.7077, -1.7096, -1.2595, 0.1521, 1.1007, 0.5501, 1.6688, -0.0638, 0.0076, -0.5855, 2.1850, 1.0925, 0.1318, -1.3908, 1.6405, -1.4660, 0.9449, 1.1731, -0.2352, -1.2735, 0.1057, -0.4734, 0.7950, 0.7992, -2.0543, -0.5895, -0.8830, 0.2309, 0.0573, 0.3862, 1.4217, -0.3348, 0.3321, -0.3124, -0.6966, 0.7180, 2.1535, -0.0927, -0.0408, 0.8648, -1.6897, 0.0164, 0.2011, -1.6373, -1.3301, -2.0066, 0.2982, -0.9084, 1.4774, 2.0907, 1.7221, 0.0120, -1.7943, -0.7969, -0.7139, -1.6832, -0.6856, 1.3496, -0.7076, -0.9703, 0.7412, -1.1069, 0.2349, -0.0342, -0.2814, 0.2097, 0.9395, -1.6807, 0.3510, -0.7744, -1.1397, -0.6819, 0.9646, 1.1049, 0.6275, -0.4659, -1.1170, 0.7925, -1.1862, 0.2916, 0.4295, 2.1874, 1.6293, 0.3402, 0.8669, -0.1764, -1.1891, -0.1184, 0.8912, 0.1755, -1.3747, 0.1716, -0.0424, 0.9297, 1.1828, 0.3381, -0.7638, 0.1467, -1.2277, -0.7407, -0.7713, 0.4687, 1.2093, 1.2227, 0.1211, -0.5426, 0.1785, -0.5765, 1.0289, -0.1408, -0.1425, -0.2911, -1.6206, 0.4882, 0.9602, -0.5745, -0.4812, 1.4285, -0.3083, -1.2959, -0.0884, 1.5945, 1.3686, 0.3653, 0.5463, 1.9287, -0.1498, -0.3264, 0.4773, 1.2574, 1.2688, -0.4986, -2.4393, -1.4042, 0.3622, -0.4667, -0.7733, -0.7286, 1.2588, -0.3811, -0.0477, 1.3024, 1.3189, -0.3249, 1.5576, 0.7129, -0.6563, -1.0095, -0.0574, 1.3239, 0.1195, 0.7177, 1.2734, 1.0614, 0.5284, 0.4755, 0.2293, 1.0615, 2.4212, 0.3475, 0.2001, -0.8964, 1.1947, 0.2934, -0.8635, 0.8201, 0.5892, 1.3187, -0.6139, 1.2460, -1.1653, 2.0124, -0.5268, -1.3603, 1.3776, -1.2013, -0.9012, -0.6842, -0.4867, -0.2491, 0.5009, -1.4166, -1.1596, 0.8346, 1.9012, 1.2558, 0.4377, 0.3253, 0.2730, -0.2185, 0.1459, -0.6556, 0.8834, 0.9182, 0.5460, 0.8974, 0.3454, 1.4419, -0.6277, -0.5550, 1.4637, -1.2075, 0.0351, -0.0763, -1.0678, -1.1450, -1.7627, 1.5489, -1.3139, 0.5900, 1.8291, -0.7769, 0.2360, 0.9665, 0.4738, 1.2182, -0.4973, 1.8134, -0.4406, 1.5517, -0.6078, 0.6166, 0.3546, 3.3038, -1.3347, 0.1461, 1.7339, 0.7130, 0.9821, -0.8946, -0.0587, -1.0436, 2.0158, 0.4382, 0.6684, -0.2568, 2.1630, -0.2076, 0.1284, 1.0824, 2.5181, 1.4952, -0.3011, 0.0344, 0.5201, 0.8041, 0.0998, 0.0420, 0.2468, 0.3540, -0.0992, 0.3281, -0.7512, -0.8791, 0.9231, 0.4910, -0.0341, -1.0569, -0.4935, 1.2465, -1.2383, -0.2076, -0.5715, -1.3184, 0.5335, -0.2962, 0.5552, -1.0107, -0.2017, 1.2928, -1.1122, 1.4421, 1.1686, 0.1797, 0.1249, 0.0315, -0.0732, 0.3209, -0.5786, -1.1185, 0.7188, -1.6608, 0.9858, 0.0564, -0.7983, 1.4472, 0.2811, -0.0707, -0.1365, -1.1118, 0.8779, 0.9292, 0.7270, -0.2922, -1.2146, 0.8220, 0.8258, 1.3501, -1.1697, -1.3437, 0.9935, 1.3896, -0.0500, 0.4315, 0.6646, 0.3518, 0.7530, 0.4446, -1.3549, -1.5216, -0.5152, 0.3380, -1.1908, 0.9378, 1.9747, 1.2941, -0.1856, 1.6567, 0.9999, -0.9011, -1.0282, -0.9657, 0.1740, -1.3632, 0.0073, 2.0704, -0.8866, 0.5801, -0.4298, 1.5373, -1.3765, 1.0940, 1.1220, -1.9517, -2.0081, 0.7610, -0.4964, -1.3645, -0.3989, 0.9138, -0.1875, 0.2259, -0.8132, 1.0139, -0.3494, 0.1600, -0.1128, -0.7475, 0.1741, 0.9152, -0.6052, -0.2428, -0.1142, 0.7598, -0.8855, 1.5841, -1.5744, 1.0301, -2.0041, -0.2319, -0.9215, -0.2888, 1.4342, -0.0543, -1.7574, -1.2878, 0.6489, 1.5510, 0.9286, -0.8614, -0.1038, 0.0857, -0.1356, 0.3840, -1.8969, 0.4572, 0.8997, -0.5483, 0.6568, -1.1300, 0.4166, 0.2141, 1.0271, -1.2323, 0.3386, 1.4328, 0.4474, 1.4298, -0.6357, -0.8917, -0.2301, -0.7127, -0.3144, 0.0256, -0.9786, -0.3012, 0.6395, 0.1282, 0.6014, 1.1896, 1.3624, 0.7118, 0.8199, 0.7963, 0.2482, 0.4524, 0.0867, -0.5367, -0.9734, -0.1216, -0.1936, 0.0713, -0.0036, 0.5986, -1.6296, -0.8290, 0.4495, -0.5734, 0.7320, 0.1898, 1.3031, -1.2163, -1.7332, 0.3483, -1.3599, 1.6499, 0.4444, 0.4270, 0.2522, 0.0010, -0.7212, -1.8166, -0.2547, -0.6889, 0.8798, -1.7457, -0.5444, 0.4527, 0.4351, -2.2924, 0.5246, 1.6748, -0.8868, 0.0817, -1.7841, 0.8100, 0.3587, 0.3137, -0.1885, 1.2921, 0.4185, 0.0997, -2.7778, -0.4596, 0.1096, 0.7805, 1.9254, 0.1675, 0.6355, 0.1979, -0.9808, 0.5720, -0.2461, -0.6234, 1.3552, 0.2213, -0.1517, 0.3012, -0.0793, 2.0024, -0.0616, -1.3867, -0.1012, 0.8794, 0.9302, -0.7258, -1.4794, -0.6096, 1.9274, 1.7403, 1.3437, -0.8758, 1.4665, 0.1013, -2.4471, -0.6855, -1.6839, 0.0124, -1.4907, -0.8691, -1.6472, 1.4398, -0.6567, 0.3457, -1.3221, -0.5548, 0.3251, -0.3363, 0.1572, -0.1570, 0.8605, 0.7022, 0.1339, 0.2422, -0.7059, 0.7512, -2.7724, -1.2306, -0.1360, 1.6061, 0.5548, 2.3874, 0.8066, -0.7024, 0.1064, 0.4123, 0.6609, 1.1174, -1.3614, 0.5984, -1.4932, 0.9754, -1.3446, -0.7697, -0.7786, -0.7728, 0.0491, -1.3113, 0.6028, -1.6903, -0.5761, 0.3289, 0.0822, 0.2504, -0.4108, -0.1314, -1.3091, 0.5963, -2.3381, 2.7258, -0.9036, 0.4295, 0.8970, -0.3366, 1.8257, -1.9538, -0.5138, -0.2476, 0.9616, -0.3764, -0.0753, 1.0649, 1.3591, -0.0917, 0.4533, -1.7148, 1.1148, 1.3446, -0.9208, 0.8313, -1.7061, -0.5698, -0.2720, -1.1354, -0.3183, -0.1104, 0.8636, 0.7355, 0.4755, -0.3639, 0.6745, -1.6300, 0.0276, 0.9693, 1.1585, -0.2953, -0.9446, 1.3311, -1.1145, -0.6080, -1.0900, 0.1887, -2.1673, 1.3874, -0.7683, -0.5039, -1.4517, -0.3553, 0.0398, 0.8291, -1.0323, -0.4918, -0.0018, -1.3269, 0.1695, 0.7246, 0.6451, -1.2770, -0.5093, -1.6237, -0.5615, 0.3414, 0.3050, 1.5664, -1.1581, 0.7162, -0.7971, 1.2511, 0.2236, -0.4719, 0.4693, -1.0829, 0.2867, -0.6070, -0.3745, 0.7811, 0.1815, -1.1424, -1.2468, 0.3300, -1.1992, 1.8581, -1.8105, -0.5393, -1.3511, 0.0585, -0.6193, -0.0706, -0.6510, 1.7633, -0.9685, 0.7483, 0.4997, -0.9258, -0.0254, 2.1944, 0.3834, -0.6556, -0.1829, 0.1604, 0.9760, -0.2839, -1.3385, 1.9360, 1.1617, -1.0418, 0.3017, -0.2769, 0.1426, 1.7173, 0.8555, 1.6163, 0.4028, -1.0660, -1.7338, -2.6045, 1.4936, -1.3113, 0.6336, -0.1844, -0.4594, -2.1543, -2.7912, 0.6503, 1.2525, 0.3034, 0.5539, 0.7960, 0.8876, 0.0987, 1.1880, 0.7207, -2.1114, -0.5423, -1.2605, -1.5198, -1.4271, 0.5086, 1.0656, 0.7067, -0.3965, 2.1617, -0.0877, -0.6930, 0.1145, -1.1114, 0.7172, -0.7010, -0.6587, 0.9099, -0.6009, 0.3170, -0.4685, 0.1693, 0.7370, -1.2185, 0.3960, -0.9955, 0.1137, 1.3158, -0.2656, 0.2954, 0.4124, 0.1692, -0.9509, 0.3719, 0.0004, 0.7079, -1.1111, 0.1898, 0.2769, 2.0242, 2.7261, 0.7454, -0.1794, -0.8380, 2.0237, 0.8175, 0.2677, -0.4527, -1.5888, -0.4369, -0.2146, -0.0742, 0.5577, 0.4029, -1.6698, -0.4652, 1.0632, 0.5684, -0.1384, 0.8865, -0.4867, -1.1607, -0.0829, 0.2570, -1.3906, -0.6902, -1.2579, 1.6785, 0.2998, -0.7169, 0.3991, 1.1934, 0.2872, -0.5040, -1.1056, -1.0176, 0.0678, 0.3098, -0.3105, 2.0015, 0.5594, 0.1860, -0.5092, 1.7572, 1.5416, -1.1598, -0.2009, -1.2830, -0.1299, 0.8729, 0.2709, -0.8878, 0.4449, 1.8526, 1.2371, -0.6526, -1.1235, -0.6230, -0.8082, 0.3661, 1.1455, 0.4740, 0.0099, 0.4708, -0.9292, -0.2106, 1.6123, -0.2588, 2.4082, -3.3332, -0.2062, -0.7034, -0.8431, -1.0063, -1.1958, -0.7490, 0.8283, 0.7992, -0.8061, 0.5960, 1.3553, -0.2295, 1.4823, 0.5275, 1.7804, -0.1270, 0.5438, 0.7247, -1.3760, 0.7689, -1.0776, 0.0806, -1.2345, 0.1450, 0.4752, 1.1223, -0.3533, 1.2373, 0.8164, -2.8355, 1.1121, 1.4758, 1.5218, 1.1614, -0.3342, 1.6535, -1.2033, -0.3330, 0.4619, 0.2502, -1.2214, -0.9454, 0.7350, -1.5043, 0.9168, 0.8106, -1.2715, 1.7141, 0.6000, 0.6872, 0.3520, -0.5903, 1.1712, -0.7990, -0.4714, 0.3901, -1.4815, 0.7595, 0.3066, -0.1808, -0.3736, 0.3541, -0.1024, 0.0189, 1.0706, 0.3578, -0.6393, -0.1309, -0.0501, 0.6879, 0.2182

1,000 values from a normal distribution with μ = 0 and σ = 1, drawn by the Box–Muller transform from the seed above. This sample has mean ≈ 0.0248 and standard deviation ≈ 1.0062; small samples wander from the targets by roughly σ/√n.

설정

빠른 설정

The centre of the distribution. Half the values land above it, half below.

The spread. About 68% of values land within one σ of the mean and 95% within two.

Set to 0 for whole numbers.

Clipping piles values up exactly on the bound; redrawing gives a genuine truncated normal. Both change the distribution.

Only used when tail handling is set to clip or redraw.

The same seed and settings always produce the same numbers, so a shared link shows the same result.

모양 미세 조정

먼저 이미지 옆의 설정을 고르세요. 아래 조절기가 그것을 다듬습니다.

Frame

A border drawn inside the edge of the image.

고급

결과

1,000개 값

-1.5544, 0.0929, -2.0042, -0.1948, 0.0092, -0.2858, -0.5520, 0.6205, -0.3037, -0.2576, -0.0586, 0.2810, -0.1841, 0.0970, 0.5389, -0.1473, -2.3349, 0.9118, -0.5039, -0.4622, 0.7363, -1.2305, 0.3360, -0.0160, 0.4424, 0.6366, 1.3528, -1.0821, -0.2941, 0.2322, -0.9358, 0.3435, 2.2647, -1.1814, -0.6401, 0.8638, -1.4141, 1.2517, -0.6194, 0.5495, 0.1093, 0.4490, 0.7460, 0.5181, -0.7512, -0.1976, 0.8679, -0.8928, 0.3566, 0.5879, -1.1012, 0.1503, 0.0558, 0.1697, -0.9147, 0.5898, 1.8092, -0.9823, -1.9892, -1.8641, 1.1496, 0.1162, 1.2656, -0.6282, 0.0079, 0.7121, -0.3091, 1.9971, -1.3092, -0.5735, 0.3698, 0.2574, -1.4204, -1.8355, -2.3185, -1.5133, -0.6620, -0.3070, -0.2509, -0.9381, 2.0731, -1.5373, -0.7029, -0.7740, -0.0122, -0.6580, -0.6122, -0.0336, -1.3017, 0.0735, 1.6390, 0.6890, -0.5200, -0.4487, 0.2087, 0.3402, -1.4661, 0.1020, 0.1711, -0.3281, 1.0617, 1.4287, 1.5392, 0.3186, 1.5202, -0.0326, -0.1735, -0.3404, -1.4086, 0.3481, 1.5157, 0.7770, -0.9417, 1.5894, -1.4699, 0.1985, -1.2916, -0.5854, -1.0773, -0.3245, -0.0161, 0.4242, 0.3004, 0.4774, -0.7928, 0.2363, 1.6275, 1.0561, -0.6351, 1.1408, -0.8967, 0.5092, 0.2485, -0.1887, -0.4369, 1.2757, -0.1130, -0.2910, -1.0593, -0.7217, 1.4687, -0.1655, 0.3707, 1.9257, -1.1297, 0.1360, -0.3535, 0.7077, -1.7096, -1.2595, 0.1521, 1.1007, 0.5501, 1.6688, -0.0638, 0.0076, -0.5855, 2.1850, 1.0925, 0.1318, -1.3908, 1.6405, -1.4660, 0.9449, 1.1731, -0.2352, -1.2735, 0.1057, -0.4734, 0.7950, 0.7992, -2.0543, -0.5895, -0.8830, 0.2309, 0.0573, 0.3862, 1.4217, -0.3348, 0.3321, -0.3124, -0.6966, 0.7180, 2.1535, -0.0927, -0.0408, 0.8648, -1.6897, 0.0164, 0.2011, -1.6373, -1.3301, -2.0066, 0.2982, -0.9084, 1.4774, 2.0907, 1.7221, 0.0120, -1.7943, -0.7969, -0.7139, -1.6832, -0.6856, 1.3496, -0.7076, -0.9703, 0.7412, -1.1069, 0.2349, -0.0342, -0.2814, 0.2097, 0.9395, -1.6807, 0.3510, -0.7744, -1.1397, -0.6819, 0.9646, 1.1049, 0.6275, -0.4659, -1.1170, 0.7925, -1.1862, 0.2916, 0.4295, 2.1874, 1.6293, 0.3402, 0.8669, -0.1764, -1.1891, -0.1184, 0.8912, 0.1755, -1.3747, 0.1716, -0.0424, 0.9297, 1.1828, 0.3381, -0.7638, 0.1467, -1.2277, -0.7407, -0.7713, 0.4687, 1.2093, 1.2227, 0.1211, -0.5426, 0.1785, -0.5765, 1.0289, -0.1408, -0.1425, -0.2911, -1.6206, 0.4882, 0.9602, -0.5745, -0.4812, 1.4285, -0.3083, -1.2959, -0.0884, 1.5945, 1.3686, 0.3653, 0.5463, 1.9287, -0.1498, -0.3264, 0.4773, 1.2574, 1.2688, -0.4986, -2.4393, -1.4042, 0.3622, -0.4667, -0.7733, -0.7286, 1.2588, -0.3811, -0.0477, 1.3024, 1.3189, -0.3249, 1.5576, 0.7129, -0.6563, -1.0095, -0.0574, 1.3239, 0.1195, 0.7177, 1.2734, 1.0614, 0.5284, 0.4755, 0.2293, 1.0615, 2.4212, 0.3475, 0.2001, -0.8964, 1.1947, 0.2934, -0.8635, 0.8201, 0.5892, 1.3187, -0.6139, 1.2460, -1.1653, 2.0124, -0.5268, -1.3603, 1.3776, -1.2013, -0.9012, -0.6842, -0.4867, -0.2491, 0.5009, -1.4166, -1.1596, 0.8346, 1.9012, 1.2558, 0.4377, 0.3253, 0.2730, -0.2185, 0.1459, -0.6556, 0.8834, 0.9182, 0.5460, 0.8974, 0.3454, 1.4419, -0.6277, -0.5550, 1.4637, -1.2075, 0.0351, -0.0763, -1.0678, 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1,000 values from a normal distribution with μ = 0 and σ = 1, drawn by the Box–Muller transform from the seed above. This sample has mean ≈ 0.0248 and standard deviation ≈ 1.0062; small samples wander from the targets by roughly σ/√n.

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-1.5544, 0.0929, -2.0042, -0.1948, 0.0092, -0.2858, -0.5520, 0.6205, -0.3037, -0.2576, -0.0586, 0.2810, -0.1841, 0.0970, 0.5389, -0.1473, -2.3349, 0.9118, -0.5039, -0.4622, 0.7363, -1.2305, 0.3360, -0.0160, 0.4424, 0.6366, 1.3528, -1.0821, -0.2941, 0.2322, -0.9358, 0.3435, 2.2647, -1.1814, -0.6401, 0.8638, -1.4141, 1.2517, -0.6194, 0.5495, 0.1093, 0.4490, 0.7460, 0.5181, -0.7512, -0.1976, 0.8679, -0.8928, 0.3566, 0.5879, -1.1012, 0.1503, 0.0558, 0.1697, -0.9147, 0.5898, 1.8092, -0.9823, -1.9892, -1.8641, 1.1496, 0.1162, 1.2656, -0.6282, 0.0079, 0.7121, -0.3091, 1.9971, -1.3092, -0.5735, 0.3698, 0.2574, -1.4204, -1.8355, -2.3185, -1.5133, -0.6620, -0.3070, -0.2509, -0.9381, 2.0731, -1.5373, -0.7029, -0.7740, -0.0122, -0.6580, -0.6122, -0.0336, -1.3017, 0.0735, 1.6390, 0.6890, -0.5200, -0.4487, 0.2087, 0.3402, -1.4661, 0.1020, 0.1711, -0.3281, 1.0617, 1.4287, 1.5392, 0.3186, 1.5202, -0.0326, -0.1735, -0.3404, -1.4086, 0.3481, 1.5157, 0.7770, -0.9417, 1.5894, -1.4699, 0.1985, -1.2916, 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아래의 배경 설명은 아직 번역되지 않아 영어로 표시됩니다.

정규분포를 따르는 수 소개

The curve arrived before the man it is named after. Abraham de Moivre, a Huguenot who left France after the revocation of the Edict of Nantes and spent the rest of his life tutoring in London coffee houses, wanted a way to approximate the enormous binomial coefficients that appear when you ask how likely a fair coin is to land heads within a given range in a thousand tosses. In a Latin pamphlet dated 12 November 1733, reprinted in the 1738 second edition of The Doctrine of Chances, he produced the approximation — an exponential of a negative square — along with the observation that the spread grows with the square root of the number of trials. Pierre-Simon Laplace generalised the argument into what is now the central limit theorem, publishing the decisive memoir in 1810.

Carl Friedrich Gauss reached the same function from the other direction. In Theoria Motus Corporum Coelestium (1809) his concern was astronomical measurement — he had made his name in 1801 by computing where the newly lost asteroid Ceres would reappear — and he asked which law of errors would make the familiar arithmetic mean the best estimate of a quantity. The answer was the same bell-shaped curve, which he tied to the method of least squares. Adrien-Marie Legendre had published least squares first, in 1805, and the priority dispute that followed was bitter; Gauss insisted he had been using the method since 1795. Stephen Stigler later made the episode a specimen of what he called the law of eponymy.

The word "normal" came later still, used independently in the 1870s by Charles Sanders Peirce, Francis Galton and Wilhelm Lexis, then cemented by Karl Pearson — who publicly regretted it, since it implies every other distribution is abnormal. Galton supplied the physical demonstration: the quincunx, a board of staggered pins down which lead shot fell into columns and piled up into a bell.

Generating such values on a computer is newer. In 1958 George Box and Mervin Muller published a two-page note in the Annals of Mathematical Statistics showing that a pair of uniform random numbers becomes a pair of independent normal ones with a logarithm, a square root and a cosine. That transform is what this page runs.

주요 성질

  • The density is f(x) = (1/(σ√(2π)))·e^(−(x−μ)²/(2σ²)); it is symmetric about μ, so the mean, median and mode all coincide there.
  • About 68.27% of values fall within one standard deviation of the mean, 95.45% within two and 99.73% within three.
  • The quartiles sit at μ ± 0.6745σ, which makes the interquartile range roughly 1.349σ.
  • Skewness and excess kurtosis are both exactly zero, and among all distributions with a given mean and variance the normal has the largest differential entropy.
  • Adding independent normal variables gives another normal one: N(μ₁, σ₁²) + N(μ₂, σ₂²) = N(μ₁+μ₂, σ₁²+σ₂²).
  • The cumulative distribution function has no elementary closed form; it is written with the error function as Φ(x) = ½·[1 + erf(x/√2)].
  • Box–Muller turns independent uniforms U₁, U₂ on (0,1) into two independent standard normals, √(−2·ln U₁)·cos(2πU₂) and √(−2·ln U₁)·sin(2πU₂); this page uses the cosine branch.
  • By the central limit theorem, the standardised sum of many independent variables with finite variance tends to this distribution whatever the variables themselves look like.

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