Distribution normale N(0, 1)

Lecture de la table

X désignant une variable aléatoire normale d'espérance 0 et d'écart-type 1, c'est-à-dire une variable aléatoire normale centrée réduite, on peut lire dans la table ci-dessous, par exemple, que

P(X ≤ -1.28155) = 10 % P(X ≤ 1.28155) = 90 %

Interprétation

Au moyen de la règle

P(a < X ≤ b) = P(X ≤ b) - P(X ≤ a)

on peut déduire, par exemple, que

P(-1.28155 < X ≤ 1.28155) = (90 %) - (10 %) = 80 %

-2.32635 → 0.01	-2.05375 → 0.02	-1.88079 → 0.03
-1.75069 → 0.04	-1.64485 → 0.05	-1.55477 → 0.06
-1.47579 → 0.07	-1.40507 → 0.08	-1.34076 → 0.09
-1.28155 → 0.1	-1.22653 → 0.11	-1.17499 → 0.12
-1.12639 → 0.13	-1.08032 → 0.14	-1.03643 → 0.15
-0.994458 → 0.16	-0.954165 → 0.17	-0.915365 → 0.18
-0.877896 → 0.19	-0.841621 → 0.2	-0.806421 → 0.21
-0.772193 → 0.22	-0.738847 → 0.23	-0.706303 → 0.24
-0.67449 → 0.25	-0.643345 → 0.26	-0.612813 → 0.27
-0.582842 → 0.28	-0.553385 → 0.29	-0.524401 → 0.3
-0.49585 → 0.31	-0.467699 → 0.32	-0.439913 → 0.33
-0.412463 → 0.34	-0.38532 → 0.35	-0.358459 → 0.36
-0.331853 → 0.37	-0.305481 → 0.38	-0.279319 → 0.39
-0.253347 → 0.4	-0.227545 → 0.41	-0.201893 → 0.42
-0.176374 → 0.43	-0.150969 → 0.44	-0.125661 → 0.45
-0.100434 → 0.46	-0.07527 → 0.47	-0.050154 → 0.48
-0.025069 → 0.49	0. → 0.5	0.025069 → 0.51
0.050154 → 0.52	0.07527 → 0.53	0.100434 → 0.54
0.125661 → 0.55	0.150969 → 0.56	0.176374 → 0.57
0.201893 → 0.58	0.227545 → 0.59	0.253347 → 0.6
0.279319 → 0.61	0.305481 → 0.62	0.331853 → 0.63
0.358459 → 0.64	0.38532 → 0.65	0.412463 → 0.66
0.439913 → 0.67	0.467699 → 0.68	0.49585 → 0.69
0.524401 → 0.7	0.553385 → 0.71	0.582842 → 0.72
0.612813 → 0.73	0.643345 → 0.74	0.67449 → 0.75
0.706303 → 0.76	0.738847 → 0.77	0.772193 → 0.78
0.806421 → 0.79	0.841621 → 0.8	0.877896 → 0.81
0.915365 → 0.82	0.954165 → 0.83	0.994458 → 0.84
1.03643 → 0.85	1.08032 → 0.86	1.12639 → 0.87
1.17499 → 0.88	1.22653 → 0.89	1.28155 → 0.9
1.34076 → 0.91	1.40507 → 0.92	1.47579 → 0.93
1.55477 → 0.94	1.64485 → 0.95	1.75069 → 0.96
1.88079 → 0.97	2.05375 → 0.98	2.32635 → 0.99