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Chi Square Calculator Online

Calculates chi square using χ² = Σ (Observed − Expected)² ÷ Expected, live in your browser.

Last updated: July 2026 · Free · No sign-up required

Results

Enter values above and click Calculate to see your result instantly.

What the Chi Square Calculator Online does

Under the hood, this calculator applies χ² = Σ (Observed − Expected)² ÷ Expected; df = categories − 1, so the chi square calculator result you see is genuine math, not a lookup table.

For context, the sections beneath the calculator include typical values, a worked example you can recompute by hand, and an FAQ covering the practical details of chi square calculator.

Quick reference

InputMeaning
Observedthe counts you actually measured
Expectedthe counts predicted under the null hypothesis
χ²sum of (O − E)² ÷ E across categories
dfnumber of categories minus 1

functions Shows the working, not just the answer

For students and teachers, the final number is only half the value. Every math tool here exposes the formula it applied, the intermediate steps, and the rounding rule, so you can follow along, check your homework, or use the answer in a proof or report with confidence.

calculate Accurate to the spec

Calculations use 64-bit floating point with sensible rounding for the domain (currency to 2 decimals, percentages to 4 decimals, algebra to 6 significant figures). Where exact rational arithmetic matters — fractions, factorials, simplification — we use a dedicated BigNumber path so 1/3 + 1/6 returns ½, not 0.49999.

school Free for classroom use

Educators are welcome to link to any math calculator on CalcaTools from a class site, Google Classroom, or worksheet. The pages are mobile-friendly, free, ad-supported (so we can keep them free) and have no sign-up wall — students just click and use them in class or at home.

tips_and_updates Pair with the spoke articles

Below the calculator we link a small set of plain-English explainer pages — "What is a percentage?", "Why does PEMDAS matter?", and so on. They cover the underlying concept in 4–6 short paragraphs. Read those before the calculator if the topic is new, or after if you want the extra context.

Interpretation guide

p-valueDecision at α = 0.05
p < 0.05Significant — observed differs from expected.
p ≥ 0.05Not significant — fits the expected distribution.
Larger χ²Bigger gap between observed and expected counts.

Formula & methodology

Formula: χ² = Σ (Observed − Expected)² ÷ Expected; df = categories − 1

The chi-square goodness-of-fit test measures how far observed counts stray from expected counts. The p-value comes from the chi-square distribution with df = categories − 1.

  1. Enter the observed counts as a comma-separated list.
  2. Enter the expected counts in the same order.
  3. The calculator sums (O − E)² ÷ E to get χ².
  4. It finds df = categories − 1 and the p-value, with a verdict at α = 0.05.

Worked example: Observed 30, 20 vs expected 25, 25 gives χ² = 2.0, df = 1, p = 0.1573 — not significant.

Frequently asked questions

How do you calculate a chi-square statistic?
Sum (observed − expected)² ÷ expected across all categories. For 30,20 vs 25,25 the χ² is 2.0.
What does the p-value tell me?
It is the probability of a χ² this large if the data truly fit the expected distribution. Below 0.05 the fit is usually rejected.
What are the degrees of freedom?
For a goodness-of-fit test, df equals the number of categories minus 1.
What is a chi-square goodness-of-fit test used for?
To check whether observed category counts match an expected distribution — like testing if a die is fair or survey results match a forecast.
Is it free and private to use?
Yes — it is 100% free, needs no sign-up, and every calculation runs in your browser, so your inputs are never uploaded or stored.
What is the chi-square distribution and when do I use it directly?
It's the distribution of a sum of k squared standard normals, where k is the degrees of freedom — always right-skewed, always non-negative, with mean k. You consult it directly for critical values: at df = 5, the 5% cutoff is 11.07, so a test statistic above that rejects at α = 0.05. Goodness-of-fit and independence tests both reduce to this lookup.

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