Top 1000 Most Popular U.S. Baby Names Of 2015

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2015 Popular Names U.S. - Top 1000 Baby Names
				Change from 2014						Change from 2014
Girls		Number of Girls	%	Rank	Number	Boys		Number of Boys	%	Rank	Number
1	Emma	20,455	1.051	0	-481	1	Noah	19,635	0.962	0	330
2	Olivia	19,691	1.011	0	-116	2	Liam	18,374	0.901	0	-88
3	Sophia	17,417	0.895	0	-1,192	3	Mason	16,627	0.815	0	-574
4	Ava	16,378	0.841	1	682	4	Jacob	15,949	0.782	0	-934
5	Isabella	15,617	0.802	-1	-1,472	5	William	15,909	0.780	0	-911
6	Mia	14,905	0.766	0	1,393	6	Ethan	15,077	0.739	0	-647
7	Abigail	12,401	0.637	1	316	7	James	14,824	0.727	2	393
8	Emily	11,786	0.605	-1	-861	8	Alexander	14,547	0.713	0	-867
9	Charlotte	11,398	0.585	1	1,283	9	Michael	14,431	0.707	-2	-1,023
10	Harper	10,295	0.529	1	687	10	Benjamin	13,700	0.672	2	-101
11	Madison	10,088	0.518	-2	-232	11	Elijah	13,651	0.669	0	-181
12	Amelia	9,861	0.506	3	1,066	12	Daniel	13,506	0.662	-2	-431
13	Elizabeth	9,730	0.500	1	166	13	Aiden	13,461	0.660	1	59
14	Sofia	9,690	0.498	-2	93	14	Logan	12,919	0.633	-1	-729
15	Evelyn	9,363	0.481	1	616	15	Matthew	12,749	0.625	1	-163
16	Avery	9,342	0.480	-3	-226	16	Lucas	12,305	0.603	3	147
17	Chloe	7,930	0.407	1	-608	17	Jackson	12,269	0.601	0	50
18	Ella	7,902	0.406	-1	-651	18	David	11,792	0.578	0	-422
19	Grace	7,656	0.393	1	15	19	Oliver	11,655	0.571	13	2,216
20	Victoria	7,609	0.391	-1	-387	20	Jayden	11,541	0.566	-5	-1,425
21	Aubrey	7,386	0.379	0	-237	21	Joseph	11,487	0.563	-1	-631
22	Scarlett	7,131	0.366	8	1,126	22	Gabriel	10,870	0.533	2	-48
23	Zoey	6,944	0.357	-1	-467	23	Samuel	10,865	0.533	0	-130
24	Addison	6,721	0.345	0	-267	24	Carter	10,780	0.528	3	103
25	Lily	6,661	0.342	2	-112	25	Anthony	10,634	0.521	-4	-946
26	Lillian	6,612	0.340	-1	-301	26	John	10,386	0.509	0	-305
27	Natalie	6,500	0.334	-4	-605	27	Dylan	10,277	0.504	2	-133
28	Hannah	6,414	0.329	0	-171	28	Luke	10,276	0.504	0	-230
29	Aria	6,405	0.329	2	482	29	Henry	10,172	0.499	4	740
30	Layla	6,330	0.325	-1	-123	30	Andrew	10,110	0.496	-8	-1,049
31	Brooklyn	6,311	0.324	-5	-505	31	Isaac	9,964	0.488	0	-13
32	Alexa	6,052	0.311	31	1,806	32	Christopher	9,815	0.481	-2	-537
33	Zoe	6,041	0.310	-1	164	33	Joshua	9,801	0.480	-8	-1,100
34	Penelope	5,949	0.306	9	865	34	Wyatt	9,645	0.473	5	771
35	Riley	5,730	0.294	12	936	35	Sebastian	9,620	0.472	-1	314
36	Leah	5,618	0.289	-1	14	36	Owen	9,596	0.470	0	446
37	Audrey	5,603	0.288	-1	40	37	Caleb	8,801	0.431	-2	-424
38	Savannah	5,456	0.280	0	-17	38	Nathan	8,588	0.421	0	-388
39	Allison	5,346	0.275	0	-126	39	Ryan	8,526	0.418	-2	-559
40	Samantha	5,340	0.274	-7	-386	40	Jack	8,499	0.417	1	-248
41	Nora	5,323	0.273	8	585	41	Hunter	8,330	0.408	-1	-482
42	Skylar	5,281	0.271	6	511	42	Levi	8,306	0.407	3	256
43	Camila	5,274	0.271	-2	56	43	Christian	8,196	0.402	-1	-259
44	Anna	5,130	0.263	-10	-559	44	Jaxon	8,067	0.395	2	366
45	Paisley	5,078	0.261	8	584	45	Julian	8,046	0.394	2	375
46	Ariana	4,958	0.255	-9	-537	46	Landon	7,929	0.389	-3	-297
47	Aaliyah	4,863	0.250	-2	-20	47	Grayson	7,908	0.388	16	1,337
48	Ellie	4,862	0.250	7	382	48	Jonathan	7,626	0.374	-4	-480
49	Claire	4,829	0.248	-5	-185	49	Isaiah	7,613	0.373	-1	-7
50	Violet	4,813	0.247	17	629	50	Charles	7,187	0.352	1	-158

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.