Top 1000 Most Popular U.S. Baby Names Of 1956

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1956 Popular Names U.S. - Top 1000 Baby Names
				Change from 1955						Change from 1955
Girls		Number of Girls	%	Rank	Number	Boys		Number of Boys	%	Rank	Number
1	Mary	61,756	2.999	0	-1,409	1	Michael	90,656	4.225	0	2,329
2	Debra	48,302	2.345	2	-2,254	2	James	84,863	3.955	1	708
3	Linda	48,075	2.334	0	-3,213	3	Robert	83,917	3.911	1	214
4	Deborah	47,837	2.323	-2	-4,478	4	David	81,624	3.804	-2	-4,629
5	Susan	46,571	2.261	0	-831	5	John	80,790	3.766	0	736
6	Patricia	43,342	2.104	0	-2,875	6	William	58,957	2.748	0	-1,001
7	Karen	40,047	1.944	1	6,829	7	Richard	52,906	2.466	0	-639
8	Cynthia	32,344	1.570	3	5,158	8	Mark	48,832	2.276	1	6,610
9	Barbara	32,298	1.568	-2	-1,307	9	Thomas	44,818	2.089	-1	-1,006
10	Donna	29,856	1.450	0	1,913	10	Steven	38,397	1.790	0	576
11	Nancy	28,784	1.398	-2	-1,233	11	Charles	35,219	1.642	0	-129
12	Pamela	26,927	1.307	0	235	12	Joseph	32,732	1.526	1	1,139
13	Sharon	26,886	1.305	3	4,846	13	Gary	31,809	1.483	-1	-2,598
14	Sandra	24,294	1.180	-1	-817	14	Donald	28,213	1.315	0	499
15	Diane	22,510	1.093	-1	-786	15	Ronald	27,741	1.293	3	1,582
16	Carol	21,405	1.039	-1	-636	16	Kenneth	27,425	1.278	-1	687
17	Kathleen	21,370	1.038	0	-634	17	Paul	26,885	1.253	-1	208
18	Cheryl	21,276	1.033	1	2,173	18	Daniel	26,629	1.241	-1	184
19	Brenda	19,343	0.939	-1	224	19	Jeffrey	23,272	1.085	1	762
20	Kathy	17,175	0.834	2	1,851	20	Timothy	23,272	1.085	2	2,535
21	Janet	16,764	0.814	-1	-1,150	21	Larry	22,303	1.040	-2	-512
22	Elizabeth	16,345	0.794	-1	-348	22	Kevin	21,767	1.015	5	4,614
23	Denise	14,507	0.704	0	-51	23	Stephen	21,194	0.988	-2	-938
24	Teresa	14,463	0.702	2	1,338	24	Gregory	21,087	0.983	0	1,278
25	Margaret	13,918	0.676	-1	-518	25	Edward	19,415	0.905	0	149
26	Janice	13,016	0.632	-1	-156	26	Dennis	18,361	0.856	-3	-2,050
27	Rebecca	12,172	0.591	0	-222	27	George	17,227	0.803	-1	-934
28	Catherine	12,062	0.586	0	434	28	Randy	16,551	0.771	2	1,594
29	Robin	11,616	0.564	4	1,400	29	Douglas	16,359	0.762	0	1,042
30	Joyce	11,092	0.539	-1	-185	30	Anthony	15,559	0.725	1	1,388
31	Lisa	11,014	0.535	17	2,478	31	Terry	15,055	0.702	-3	-525
32	Theresa	10,845	0.527	6	1,226	32	Bruce	14,665	0.684	0	587
33	Carolyn	10,641	0.517	-3	-533	33	Brian	14,657	0.683	7	4,548
34	Laura	10,571	0.513	7	1,566	34	Scott	13,214	0.616	4	2,440
35	Judy	10,199	0.495	-3	-175	35	Jerry	12,631	0.589	-2	-343
36	Cathy	10,137	0.492	8	1,476	36	Keith	11,484	0.535	3	1,078
37	Cindy	9,984	0.485	42	4,395	37	Peter	11,326	0.528	-2	324
38	Kim	9,893	0.480	37	4,207	38	Frank	11,150	0.520	-4	48
39	Debbie	9,863	0.479	24	2,614	39	Raymond	11,099	0.517	-3	112
40	Beverly	9,453	0.459	-6	-541	40	Patrick	10,327	0.481	1	457
41	Shirley	9,429	0.458	-4	-321	41	Roger	10,141	0.473	-4	-830
42	Christine	9,377	0.455	-2	340	42	Lawrence	8,887	0.414	0	-245
43	Connie	9,364	0.455	-8	-610	43	Ricky	8,779	0.409	3	455
44	Betty	9,220	0.448	-8	-706	44	Wayne	8,723	0.407	0	1
45	Judith	9,111	0.442	-14	-1,318	45	Alan	8,719	0.406	-2	-312
46	Ann	8,987	0.436	-3	75	46	Danny	8,556	0.399	-1	-154
47	Paula	8,924	0.433	-8	-661	47	Christopher	8,376	0.390	2	471
48	Julie	8,573	0.416	19	1,787	48	Carl	8,311	0.387	-1	25
49	Gail	8,438	0.410	-7	-529	49	Gerald	7,984	0.372	-1	-84
50	Jane	8,246	0.400	0	-91	50	Dale	7,711	0.359	2	539

United States name popularity data is provided by the Social Security Administration and is based on Social Security card applications.

Data for a given year is not made available until well into the next year.

Data reflects what was recorded and has not been edited for errors, so for example the gender associated with a name may be incorrect.

The more babies that are given a particular name, the higher the popularity ranking. If multiple names have the same usage, the tie is broken by assigning popularity rank in alphabetical order. Therefore in the case of names with fewer occurrences, names with the same number of occurrences may have vastly different rankings because they will be interranked alphabetically.

To safeguard privacy, the SSA does not include names with less than 5 occurrences.

Please note, we update the data each May when the SSA releases new figures. All data changes at that time, including previous years, which will change minutely based on new information.