Top 100 Most Popular U.S. Baby Names Of 2010

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2010 Popular Names U.S. - Top 100 Baby Names
				Change from 2009						Change from 2009
Girls		Number of Girls	%	Rank	Number	Boys		Number of Boys	%	Rank	Number
1	Isabella	22,913	1.170	0	607	1	Jacob	22,127	1.078	0	952
2	Sophia	20,643	1.054	2	3,699	2	Ethan	18,002	0.877	0	-1,845
3	Emma	17,345	0.885	-1	-550	3	Michael	17,350	0.845	0	-1,590
4	Olivia	17,028	0.869	-1	-407	4	Jayden	17,179	0.837	4	-109
5	Ava	15,433	0.788	0	-440	5	William	17,051	0.831	0	-867
6	Emily	14,274	0.729	0	-1,078	6	Alexander	16,756	0.816	-2	-1,478
7	Abigail	14,247	0.727	1	-143	7	Noah	16,451	0.801	2	-796
8	Madison	13,183	0.673	-1	-2,056	8	Daniel	15,843	0.772	-1	-1,699
9	Chloe	11,754	0.600	0	-155	9	Aiden	15,538	0.757	3	-482
10	Mia	10,639	0.543	0	-787	10	Anthony	15,493	0.755	1	-814
11	Addison	10,325	0.527	1	-335	11	Joshua	15,442	0.752	-5	-2,208
12	Elizabeth	10,262	0.524	-1	-773	12	Mason	14,845	0.723	22	4,160
13	Ella	9,876	0.504	2	222	13	Christopher	14,279	0.696	-3	-2,076
14	Natalie	8,777	0.448	2	-632	14	Andrew	14,248	0.694	2	-614
15	Samantha	8,408	0.429	-1	-1,252	15	David	14,199	0.692	-1	-1,241
16	Alexis	8,265	0.422	-3	-1,669	16	Matthew	14,134	0.689	-3	-1,852
17	Lily	7,986	0.408	1	-112	17	Logan	14,022	0.683	0	-437
18	Grace	7,683	0.392	-1	-631	18	Elijah	13,900	0.677	3	1,072
19	Hailey	7,019	0.358	6	-298	19	James	13,883	0.676	-1	-325
20	Alyssa	6,991	0.357	-1	-975	20	Joseph	13,830	0.674	-5	-1,090
21	Hannah	6,988	0.357	2	-584	21	Gabriel	12,877	0.627	1	50
22	Lillian	6,951	0.355	5	94	22	Benjamin	12,431	0.606	-3	-684
23	Avery	6,674	0.341	9	376	23	Ryan	11,969	0.583	-3	-1,138
24	Leah	6,537	0.334	5	-252	24	Samuel	11,963	0.583	0	-189
25	Nevaeh	6,425	0.328	9	318	25	Jackson	11,818	0.576	1	-301
26	Anna	6,329	0.323	2	-475	26	John	11,556	0.563	1	-562
27	Sarah	6,328	0.323	-6	-1,452	27	Nathan	11,373	0.554	-2	-748
28	Sofia	6,327	0.323	8	633	28	Jonathan	11,126	0.542	1	-247
29	Ashley	6,314	0.322	-9	-1,499	29	Christian	11,097	0.541	-6	-1,545
30	Brianna	6,277	0.320	-6	-1,075	30	Liam	10,927	0.532	19	2,363
31	Zoe	6,269	0.320	16	1,123	31	Dylan	10,512	0.512	-1	-852
32	Victoria	6,227	0.318	-1	-134	32	Caleb	10,473	0.510	-1	-519
33	Gabriella	6,178	0.315	0	33	33	Landon	10,473	0.510	3	310
34	Brooklyn	6,122	0.313	3	458	34	Tyler	10,447	0.509	-6	-1,095
35	Kaylee	6,098	0.311	-9	-1,115	35	Lucas	10,377	0.506	4	765
36	Taylor	5,896	0.301	-14	-1,687	36	Evan	9,730	0.474	-1	-547
37	Layla	5,891	0.301	8	685	37	Nicholas	9,641	0.470	-5	-1,209
38	Allison	5,864	0.299	-8	-717	38	Gavin	9,618	0.469	-5	-1,119
39	Evelyn	5,840	0.298	1	257	39	Isaac	9,357	0.456	1	-196
40	Riley	5,538	0.283	-2	-97	40	Brayden	9,111	0.444	8	515
41	Amelia	5,455	0.278	14	756	41	Luke	8,842	0.431	6	224
42	Khloe	5,405	0.276	53	1,950	42	Angel	8,770	0.427	-5	-1,079
43	Makayla	5,394	0.275	1	130	43	Isaiah	8,577	0.418	-2	-663
44	Savannah	5,373	0.274	-5	-223	44	Brandon	8,545	0.416	-6	-1,296
45	Aubrey	5,360	0.274	-4	-46	45	Jack	8,518	0.415	-3	-505
46	Charlotte	5,357	0.273	22	1,169	46	Jordan	8,237	0.401	-1	-415
47	Zoey	5,208	0.266	28	1,214	47	Owen	8,175	0.398	4	36
48	Bella	5,123	0.262	10	589	48	Carter	8,156	0.397	2	-11
49	Kayla	5,055	0.258	-14	-785	49	Connor	8,050	0.392	3	-48
50	Alexa	5,040	0.257	-7	-268	50	Justin	7,846	0.382	-4	-778

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.