Chi-square test calculator

Chi-square test calculator for goodness of fit and independence in a contingency table: χ², p-value, expected counts and Cramér's V.

ਅੱਪਡੇਟ ਕੀਤਾ ਜਾਂਚੀਆਂ ਉਦਾਹਰਨਾਂ: 5

One count per category, e.g. how often each face of a die came up.
ਹੋਰ ਚੋਣਾਂ
Each one removes a degree of freedom (e.g. 1 when fitting a Poisson mean).
ਅਜ਼ਮਾਓ
p-value
p-value: 0.578555
ਵੱਧ ਤੋਂ ਵੱਧ ਦਸ਼ਮਲਵ ਥਾਵਾਂ: 6; ਸਭ ਤੋਂ ਨੇੜੇ; ਬਰਾਬਰ ਦੂਰੀ ਉੱਤੇ ਸਿਫ਼ਰ ਤੋਂ ਦੂਰ
Decision
Fail to reject H₀
χ² statistic
3.8
Degrees of freedom
5
Critical value
11.0705
Smallest expected count
20

Fail to reject H₀ at α = 0.05. If every category were equally likely, a result at least this extreme would turn up with probability 0.5786. The p-value is not the probability that H₀ is true, and not rejecting H₀ does not show it is true — the sample may be too small to detect a real effect. The largest contribution comes from category 1 (observed 15, expected 20).

p-value area under χ² with 5 df

00.050.10.1505101520χ²Densityχ² = 3.8critical 11.07

χ² contribution per category, observed vs expected (blue: above, orange: below)

1: 15 vs 201.252: 22 vs 200.23: 18 vs 200.24: 25 vs 201.255: 17 vs 200.456: 23 vs 200.45
Observed, expected and contribution to χ² ਕਤਾਰਾਂ: 6
CategoryObservedExpected(O − E)²/E
115201.25
222200.2
318200.2
425201.25
517200.45
623200.45
ਗਣਨਾ ਕਿਵੇਂ ਹੁੰਦੀ ਹੈ S
  1. Hypotheses

    H0:counts follow the equal proportions,H1:they don’tH_0: \text{counts follow the equal proportions},\quad H_1: \text{they don't}
  2. Expected counts

    Ei=N wi∑w⇒E=(20, 20, 20, 20, 20, 20)E_i = N\,\frac{w_i}{\sum w} \Rightarrow E = (20,\ 20,\ 20,\ 20,\ 20,\ 20)
  3. Statistic

    χ2=∑(O−E)2E=1.25+0.2+0.2+1.25+0.45+0.45=3.8\chi^2 = \sum \frac{(O-E)^2}{E} = 1.25 + 0.2 + 0.2 + 1.25 + 0.45 + 0.45 = 3.8
  4. Degrees of freedom

    ν=k−1−m=5\nu = k - 1 - m = 5
  5. p-value

    p=P(χ52≥3.8)=0.57855529p = P(\chi^2_{5} \ge 3.8) = 0.57855529
  6. Decision

    p>α=0.05⇒do not reject H0p > \alpha = 0.05 \Rightarrow \text{do not reject } H_0

    Equivalently, compare χ² with the critical value 11.0705.

Chi-square test calculator ਬਾਰੇ

Pearson's chi-square test compares observed counts with the counts a hypothesis predicts: χ² = Σ(O − E)²/E. The goodness-of-fit test takes the expected counts from equal or given proportions and has k − 1 degrees of freedom, one fewer for each parameter estimated from the data. The test of independence sets E = row total × column total ÷ grand total for each cell of a contingency table and has (r − 1)(c − 1) degrees of freedom.

It answers questions such as whether a die is fair or whether a preference depends on region. The default 120 rolls (15, 22, 18, 25, 17, 23) give χ² = 3.8 on 5 df and p = 0.579, far below the 11.07 needed at α = 0.05, so the counts are consistent with a fair die.

The χ² approximation needs expected counts of about 5 or more. Cramér's V, from 0 to 1, measures how strong an association in a table is.

ਹੱਲ ਕੀਤੀਆਂ ਉਦਾਹਰਨਾਂ

Is the die fair? (defaults)

Test
Goodness of fit
Observed counts
15, 22, 18, 25, 17, 23
Expected distribution
Equal in every category
Significance level α
0.05
χ² statistic
3.8
Degrees of freedom
5
p-value
0.578555
Decision
Fail to reject H₀
Critical value
11.0705

ਜਾਂਚ ਦਾ ਸਰੋਤ: χ² by hand: Σ(O − 20)²/20 = 76/20; p from the A&S 26.4.4 closed form for odd ν (pyref.chi2_sf_int); χ²₀.₉₅,₅ = 11.070 (χ² table)

Mendel's peas vs 9:3:3:1

Test
Goodness of fit
Observed counts
315 108 101 32
Expected distribution
Given ratios or counts
Expected ratios or counts
9 3 3 1
Significance level α
0.05
χ² statistic
0.470024
Degrees of freedom
3
p-value
0.925426

ਜਾਂਚ ਦਾ ਸਰੋਤ: Classic textbook example (χ² ≈ 0.47, p ≈ 0.93); exact χ² with Python fractions, p from A&S 26.4.4

2×2 table

Test
Independence
Contingency table
20 30 30 20
Significance level α
0.05
χ² statistic
4
Degrees of freedom
1
p-value
0.0455
Cramér's V
0.2

ਜਾਂਚ ਦਾ ਸਰੋਤ: All E = 25, χ² = 4·25/25 = 4; p = erfc(√2) = 0.0455003 (Python math.erfc); V = √(4/100)

2×3 table (defaults)

Test
Independence
Contingency table
42 33 25 28 37 35
Significance level α
0.05
χ² statistic
4.695238
Degrees of freedom
2
p-value
0.095597

ਜਾਂਚ ਦਾ ਸਰੋਤ: Expected counts R·C/N and χ² with Python fractions; p = e^(−χ²/2) for ν = 2 (A&S 26.4.5)

ਸਵਾਲ

What does the chi-square p-value mean?

It is the probability of a χ² statistic at least as large as the one observed if the null hypothesis holds, such as a fair die or independent rows and columns. For the default rolls p = 0.579: a fair die would give counts at least this uneven in about 58% of 120-roll experiments. A large p-value does not prove the die fair, since small samples can miss a real bias.

What is the minimum expected count for a chi-square test?

Cochran's (1954) rule asks that no expected count be below 1 and no more than 20% of cells be below 5. When it fails, merge sparse categories or, for a 2×2 table, use Fisher's exact test. The rule is about expected counts, not observed ones: an observed 0 is fine when its expected count is 5 or more. The warning here appears whenever any expected count is below 5.

How do you find the degrees of freedom for a chi-square test?

For goodness of fit, df = k − 1 − m, where k is the number of categories and m the number of parameters estimated from the data, so a six-sided die gives 5. For independence, df = (rows − 1) × (columns − 1): 2 for a 2×3 table and 1 for a 2×2 table. The 5% critical values for 1, 2 and 5 df are 3.841, 5.991 and 11.070.

What is Cramér's V?

Cramér's V = √(χ² / (N × (min(r, c) − 1))) rescales χ² to a 0-to-1 strength of association that does not grow with the sample size. The 2×2 worked example, χ² = 4 with N = 100, gives V = 0.2. Cohen (1988) treats 0.1, 0.3 and 0.5 as small, medium and large for a 2×2 table; tables with more rows and columns have lower thresholds.

Can I use percentages instead of counts?

No. χ² grows in proportion to the sample size, so the same percentages from 1,000 observations give 10 times the χ² of 100 observations. Entering percentages treats the sample as exactly 100 and gives the wrong p-value for any other sample size. Expected ratios, by contrast, can be on any scale: 9 3 3 1 is rescaled to the observed total.

“Chi-square test calculator” ਕਿੰਨਾ ਸਟੀਕ ਹੈ?

ਸਟੀਕਤਾ ਤੁਹਾਡੇ ਇਨਪੁੱਟਾਂ ਅਤੇ ਵਿਧੀ ਦੀਆਂ ਧਾਰਨਾਵਾਂ ਉੱਤੇ ਨਿਰਭਰ ਕਰਦੀ ਹੈ। ਦਸ਼ਮਲਵ ਗਣਨਾ 50 ਮਹੱਤਵਪੂਰਨ ਅੰਕ ਵਰਤਦੀ ਹੈ, ਪਰ ਅਨੁਮਾਨ, ਅੰਕੀ ਵਿਧੀਆਂ ਅਤੇ ਸਰੋਤ ਡਾਟਾ ਘੱਟ ਸਟੀਕ ਹੋ ਸਕਦੇ ਹਨ; ਦਿਖਾਏ ਮੁੱਲਾਂ ਨੂੰ ਗੋਲ ਕਰਨ ਨਾਲ ਇਹ ਹੱਦਾਂ ਦੂਰ ਨਹੀਂ ਹੁੰਦੀਆਂ। ਸੁਤੰਤਰ ਸਰੋਤਾਂ ਦੀਆਂ ਹੱਲ ਕੀਤੀਆਂ ਉਦਾਹਰਨਾਂ ਨਾਲ ਜਾਂਚ: 5। ਉਦਾਹਰਨ ਲਈ, “Is the die fair? (defaults)” ਦੀ ਜਾਂਚ χ² by hand: Σ(O − 20)²/20 = 76/20; p from the A&S 26.4.4 closed form for odd ν (pyref.chi2_sf_int); χ²₀.₉₅,₅ = 11.070 (χ² table) ਨਾਲ ਕੀਤੀ ਜਾਂਦੀ ਹੈ।

ਇਸ ਵਿਧੀ ਦਾ ਸਰੋਤ ਕੀ ਹੈ?

NIST/SEMATECH e-Handbook of Statistical Methods, §1.3.5.15 Chi-square goodness-of-fit test; NIST/SEMATECH e-Handbook, §7.4.5 contingency tables / test of independence; Abramowitz & Stegun, Handbook of Mathematical Functions, §26.4 (χ² probability function).

ਇਸ ਕੈਲਕੁਲੇਟਰ ਬਾਰੇ

χ2=∑(O−E)2E,Eij=Ri CjN,ν=(r−1)(c−1)\chi^2 = \sum \frac{(O - E)^2}{E},\qquad E_{ij} = \frac{R_i\,C_j}{N},\qquad \nu = (r-1)(c-1)

ਸਰੋਤ

  1. NIST/SEMATECH e-Handbook of Statistical Methods, §1.3.5.15 Chi-square goodness-of-fit test
  2. NIST/SEMATECH e-Handbook, §7.4.5 contingency tables / test of independence
  3. Abramowitz & Stegun, Handbook of Mathematical Functions, §26.4 (χ² probability function)

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ਇਸ ਕੈਲਕੁਲੇਟਰ ਵਿੱਚ ਸੁਤੰਤਰ ਸਰੋਤਾਂ ਦੇ ਜਵਾਬਾਂ ਵਾਲੀਆਂ 5 ਹੱਲ ਕੀਤੀਆਂ ਉਦਾਹਰਨਾਂ ਹਨ। ਇਹ ਟੈਸਟ ਸਮੂਹ ਵਿੱਚ ਚੱਲਦੀਆਂ ਹਨ ਅਤੇ ਤੁਸੀਂ ਇੱਥੇ ਵੀ ਚਲਾ ਸਕਦੇ ਹੋ।

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