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F Critical Values Table (F Table) — GetCalcMaster

Right-tail F critical values table for common numerator/denominator degrees of freedom (df1/df2) with α=0.10, α=0.05, and α=0.01. Includes usage notes and examples.

Use the F table to find right‑tail critical values for ANOVA, regression model comparison, and variance‑ratio tests. This page includes common α levels (0.10, 0.05, 0.01) and clear guidance on tail conventions.

Open Statistics Calculator ANOVA guide Regression guide

Educational use only. Always confirm whether your test uses a right‑tail, left‑tail, or two‑tailed rejection region.

How to read the F table

df1 = numerator degrees of freedom (columns).
df2 = denominator degrees of freedom (rows).
Most printed F tables are right-tail: they give F* where P(F ≥ F*) = α.

Tip: smaller α ⇒ larger critical value. If you see the opposite, you likely flipped tails.

F critical values (right-tail)

Values below are F* such that P(F ≥ F*) = α. Three tables are provided for common significance levels.

α = 0.10

df2 \ df1	1	2	3	4	5	6	7	8	9	10
1	39.863	49.5	53.593	55.833	57.24	58.204	58.906	59.439	59.858	60.195
2	8.526	9	9.162	9.243	9.293	9.326	9.349	9.367	9.381	9.392
3	5.538	5.462	5.391	5.343	5.309	5.285	5.266	5.252	5.24	5.23
4	4.545	4.325	4.191	4.107	4.051	4.01	3.979	3.955	3.936	3.92
5	4.06	3.78	3.619	3.52	3.453	3.405	3.368	3.339	3.316	3.297
6	3.776	3.463	3.289	3.181	3.108	3.055	3.014	2.983	2.958	2.937
7	3.589	3.257	3.074	2.961	2.883	2.827	2.785	2.752	2.725	2.703
8	3.458	3.113	2.924	2.806	2.726	2.668	2.624	2.589	2.561	2.538
9	3.36	3.006	2.813	2.693	2.611	2.551	2.505	2.469	2.44	2.416
10	3.285	2.924	2.728	2.605	2.522	2.461	2.414	2.377	2.347	2.323
11	3.225	2.86	2.66	2.536	2.451	2.389	2.342	2.304	2.274	2.248
12	3.177	2.807	2.606	2.48	2.394	2.331	2.283	2.245	2.214	2.188
13	3.136	2.763	2.56	2.434	2.347	2.283	2.234	2.195	2.164	2.138
14	3.102	2.726	2.522	2.395	2.307	2.243	2.193	2.154	2.122	2.095
15	3.073	2.695	2.49	2.361	2.273	2.208	2.158	2.119	2.086	2.059
16	3.048	2.668	2.462	2.333	2.244	2.178	2.128	2.088	2.055	2.028
17	3.026	2.645	2.437	2.308	2.218	2.152	2.102	2.061	2.028	2.001
18	3.007	2.624	2.416	2.286	2.196	2.13	2.079	2.038	2.005	1.977
19	2.99	2.606	2.397	2.266	2.176	2.109	2.058	2.017	1.984	1.956
20	2.975	2.589	2.38	2.249	2.158	2.091	2.04	1.999	1.965	1.937
21	2.961	2.575	2.365	2.233	2.142	2.075	2.023	1.982	1.948	1.92
22	2.949	2.561	2.351	2.219	2.128	2.06	2.008	1.967	1.933	1.904
23	2.937	2.549	2.339	2.207	2.115	2.047	1.995	1.953	1.919	1.89
24	2.927	2.538	2.327	2.195	2.103	2.035	1.983	1.941	1.906	1.877
25	2.918	2.528	2.317	2.184	2.092	2.024	1.971	1.929	1.895	1.866
26	2.909	2.519	2.307	2.174	2.082	2.014	1.961	1.919	1.884	1.855
27	2.901	2.511	2.299	2.165	2.073	2.005	1.952	1.909	1.874	1.845
28	2.894	2.503	2.291	2.157	2.064	1.996	1.943	1.9	1.865	1.836
29	2.887	2.495	2.283	2.149	2.057	1.988	1.935	1.892	1.857	1.827
30	2.881	2.489	2.276	2.142	2.049	1.98	1.927	1.884	1.849	1.819
40	2.835	2.44	2.226	2.091	1.997	1.927	1.873	1.829	1.793	1.763
60	2.791	2.393	2.177	2.041	1.946	1.875	1.819	1.775	1.738	1.707
120	2.748	2.347	2.13	1.992	1.896	1.824	1.767	1.722	1.684	1.652
∞	2.706	2.303	2.084	1.945	1.847	1.774	1.717	1.67	1.632	1.599

α = 0.05

df2 \ df1	1	2	3	4	5	6	7	8	9	10
1	161.448	199.5	215.707	224.583	230.162	233.986	236.768	238.883	240.543	241.882
2	18.513	19	19.164	19.247	19.296	19.33	19.353	19.371	19.385	19.396
3	10.128	9.552	9.277	9.117	9.013	8.941	8.887	8.845	8.812	8.786
4	7.709	6.944	6.591	6.388	6.256	6.163	6.094	6.041	5.999	5.964
5	6.608	5.786	5.409	5.192	5.05	4.95	4.876	4.818	4.772	4.735
6	5.987	5.143	4.757	4.534	4.387	4.284	4.207	4.147	4.099	4.06
7	5.591	4.737	4.347	4.12	3.972	3.866	3.787	3.726	3.677	3.637
8	5.318	4.459	4.066	3.838	3.687	3.581	3.5	3.438	3.388	3.347
9	5.117	4.256	3.863	3.633	3.482	3.374	3.293	3.23	3.179	3.137
10	4.965	4.103	3.708	3.478	3.326	3.217	3.135	3.072	3.02	2.978
11	4.844	3.982	3.587	3.357	3.204	3.095	3.012	2.948	2.896	2.854
12	4.747	3.885	3.49	3.259	3.106	2.996	2.913	2.849	2.796	2.753
13	4.667	3.806	3.411	3.179	3.025	2.915	2.832	2.767	2.714	2.671
14	4.6	3.739	3.344	3.112	2.958	2.848	2.764	2.699	2.646	2.602
15	4.543	3.682	3.287	3.056	2.901	2.79	2.707	2.641	2.588	2.544
16	4.494	3.634	3.239	3.007	2.852	2.741	2.657	2.591	2.538	2.494
17	4.451	3.592	3.197	2.965	2.81	2.699	2.614	2.548	2.494	2.45
18	4.414	3.555	3.16	2.928	2.773	2.661	2.577	2.51	2.456	2.412
19	4.381	3.522	3.127	2.895	2.74	2.628	2.544	2.477	2.423	2.378
20	4.351	3.493	3.098	2.866	2.711	2.599	2.514	2.447	2.393	2.348
21	4.325	3.467	3.072	2.84	2.685	2.573	2.488	2.42	2.366	2.321
22	4.301	3.443	3.049	2.817	2.661	2.549	2.464	2.397	2.342	2.297
23	4.279	3.422	3.028	2.796	2.64	2.528	2.442	2.375	2.32	2.275
24	4.26	3.403	3.009	2.776	2.621	2.508	2.423	2.355	2.3	2.255
25	4.242	3.385	2.991	2.759	2.603	2.49	2.405	2.337	2.282	2.236
26	4.225	3.369	2.975	2.743	2.587	2.474	2.388	2.321	2.265	2.22
27	4.21	3.354	2.96	2.728	2.572	2.459	2.373	2.305	2.25	2.204
28	4.196	3.34	2.947	2.714	2.558	2.445	2.359	2.291	2.236	2.19
29	4.183	3.328	2.934	2.701	2.545	2.432	2.346	2.278	2.223	2.177
30	4.171	3.316	2.922	2.69	2.534	2.421	2.334	2.266	2.211	2.165
40	4.085	3.232	2.839	2.606	2.449	2.336	2.249	2.18	2.124	2.077
60	4.001	3.15	2.758	2.525	2.368	2.254	2.167	2.097	2.04	1.993
120	3.92	3.072	2.68	2.447	2.29	2.175	2.087	2.016	1.959	1.91
∞	3.841	2.996	2.605	2.372	2.214	2.099	2.01	1.938	1.88	1.831

α = 0.01

df2 \ df1	1	2	3	4	5	6	7	8	9	10
1	4052.181	4999.5	5403.352	5624.583	5763.65	5858.986	5928.356	5981.07	6022.473	6055.847
2	98.503	99	99.166	99.249	99.299	99.333	99.356	99.374	99.388	99.399
3	34.116	30.817	29.457	28.71	28.237	27.911	27.672	27.489	27.345	27.229
4	21.198	18	16.694	15.977	15.522	15.207	14.976	14.799	14.659	14.546
5	16.258	13.274	12.06	11.392	10.967	10.672	10.456	10.289	10.158	10.051
6	13.745	10.925	9.78	9.148	8.746	8.466	8.26	8.102	7.976	7.874
7	12.246	9.547	8.451	7.847	7.46	7.191	6.993	6.84	6.719	6.62
8	11.259	8.649	7.591	7.006	6.632	6.371	6.178	6.029	5.911	5.814
9	10.561	8.022	6.992	6.422	6.057	5.802	5.613	5.467	5.351	5.257
10	10.044	7.559	6.552	5.994	5.636	5.386	5.2	5.057	4.942	4.849
11	9.646	7.206	6.217	5.668	5.316	5.069	4.886	4.744	4.632	4.539
12	9.33	6.927	5.953	5.412	5.064	4.821	4.64	4.499	4.388	4.296
13	9.074	6.701	5.739	5.205	4.862	4.62	4.441	4.302	4.191	4.1
14	8.862	6.515	5.564	5.035	4.695	4.456	4.278	4.14	4.03	3.939
15	8.683	6.359	5.417	4.893	4.556	4.318	4.142	4.004	3.895	3.805
16	8.531	6.226	5.292	4.773	4.437	4.202	4.026	3.89	3.78	3.691
17	8.4	6.112	5.185	4.669	4.336	4.102	3.927	3.791	3.682	3.593
18	8.285	6.013	5.092	4.579	4.248	4.015	3.841	3.705	3.597	3.508
19	8.185	5.926	5.01	4.5	4.171	3.939	3.765	3.631	3.523	3.434
20	8.096	5.849	4.938	4.431	4.103	3.871	3.699	3.564	3.457	3.368
21	8.017	5.78	4.874	4.369	4.042	3.812	3.64	3.506	3.398	3.31
22	7.945	5.719	4.817	4.313	3.988	3.758	3.587	3.453	3.346	3.258
23	7.881	5.664	4.765	4.264	3.939	3.71	3.539	3.406	3.299	3.211
24	7.823	5.614	4.718	4.218	3.895	3.667	3.496	3.363	3.256	3.168
25	7.77	5.568	4.675	4.177	3.855	3.627	3.457	3.324	3.217	3.129
26	7.721	5.526	4.637	4.14	3.818	3.591	3.421	3.288	3.182	3.094
27	7.677	5.488	4.601	4.106	3.785	3.558	3.388	3.256	3.149	3.062
28	7.636	5.453	4.568	4.074	3.754	3.528	3.358	3.226	3.12	3.032
29	7.598	5.42	4.538	4.045	3.725	3.499	3.33	3.198	3.092	3.005
30	7.562	5.39	4.51	4.018	3.699	3.473	3.304	3.173	3.067	2.979
40	7.314	5.179	4.313	3.828	3.514	3.291	3.124	2.993	2.888	2.801
60	7.077	4.977	4.126	3.649	3.339	3.119	2.953	2.823	2.718	2.632
120	6.851	4.787	3.949	3.48	3.174	2.956	2.792	2.663	2.559	2.472
∞	6.635	4.605	3.782	3.319	3.017	2.802	2.639	2.511	2.407	2.321

Large df approximation and interpolation

If your exact degrees of freedom are not listed, you can interpolate between nearby rows. A conservative shortcut is to round df2 down (use a smaller df2), because F critical values generally increase as df2 decreases.

When df2 is large (often ≥ 120), the table values change slowly. The df2 = ∞ row is a close approximation.
The ∞ row corresponds to the limit F_{df1,∞} = χ²_{df1}/df1 (right-tail critical values).

Micro-table (α = 0.05): df2 = 60 vs 120 vs ∞

This quick comparison shows how fast F* stabilizes as df2 grows.

df2 \ df1	1	2	5	10
60	4.001	3.15	2.368	1.993
120	3.92	3.072	2.29	1.91
∞	3.841	2.996	2.214	1.831

Micro-table (df2 = ∞): α = 0.10 vs 0.05 vs 0.01

Fast lookup when df2 is large: choose your df1 and significance level.

df1 \ α	0.10	0.05	0.01
1	2.706	3.841	6.635
2	2.303	2.996	4.605
5	1.847	2.214	3.017
10	1.599	1.831	2.321

All values are from the df2 = ∞ row (large-denominator approximation).

Download table data

CSV/JSON endpoints are fully static (/data/stats/*), cache-friendly, and marked noindex to avoid SEO duplication.

JSON (all α)CSV α=0.10 CSV α=0.05 CSV α=0.01 Manifest

Tip: for programmatic use, start with the stats-manifest.json to discover all available files.

Cite this table

Permalink: https://getcalcmaster.com/p/f-table

Last updated: 2026-03-07

GetCalcMaster. (2026-03-07). F Critical Values Table (F Table). Retrieved from {CANONICAL_URL}