Sample size for a chi-square test of independence

When to use it

Testing whether two categorical variables are related in an r × c table, such as education level and smoking status.

Not for

One variable against stated proportions. Use goodness-of-fit.
Ordered categories where a trend is expected, which a trend test detects with fewer participants.

Notation

$N$: Number of participants
$r,\ c$: Numbers of rows and columns
$P_{ij}$: Expected probability of each cell
$P_{i\cdot},\ P_{\cdot j}$: Row and column totals of the table
$w$: Cohen’s effect size
$\alpha$: Significance level: the chance of a false positive
$1-\beta$: Power: the chance of detecting the effect if it is real
$\rho$: Intracluster correlation coefficient (ICC)
$m$: Average number of participants per cluster
$\mathrm{CV}$: Coefficient of variation of cluster size
$C$: Clusters per group
$\ell$: Expected loss of whole clusters
$q$: Expected dropout

Formulas

Cohen’s w from the expected table

w = \sqrt{\sum_{i,j} \frac{(P_{ij} - P_{i\cdot}P_{\cdot j})^{2}}{P_{i\cdot}P_{\cdot j}}}

Independence means each cell is the product of its row and column totals, so the table you expect is compared with that product, not with anything you state separately [1].

The smallest N with enough power

\Pr\!\left(\chi'^{2}_{\nu,\lambda} > \chi^{2}_{\nu,\,1-\alpha}\right) \ge 1-\beta, \qquad \lambda = N w^{2}, \quad \nu = (r-1)(c-1)

A table of independence has fewer degrees of freedom than a goodness-of-fit test over the same number of cells, so it needs fewer participants at the same w.

Clustering

\mathrm{DE} = 1 + \left[(\mathrm{CV}^{2} + 1)\,m - 1\right]\rho

When participants are recruited or randomised in clusters, observations within a cluster repeat information, and the sample is multiplied by the design effect. With equal clusters (CV = 0) this is 1 + (m − 1)ρ [2, 3].

Few clusters

F_t = \left(\frac{t_{2(C-1),\,1-\alpha/\tau} + t_{2(C-1),\,1-\beta}}{z_{1-\alpha/\tau} + z_{1-\beta}}\right)^{2}

Optional. A cluster trial is analysed on cluster-level summaries, so its critical values come from t on 2(C − 1) degrees of freedom rather than the normal distribution. With few clusters this costs participants; because C depends on the sample, it is iterated until it settles [4].

All adjustments together

n_{\text{adj}} = \left\lceil n \mathrm{DE}\;F_t \right\rceil

The adjustments multiply, in this order, and the result is rounded up once. Any adjustment not used contributes a factor of 1.

Dropout

n_{\text{recruit}} = \left\lceil \frac{n_{\text{adj}}}{1 - q} \right\rceil

Applied last, to the participants actually recruited.

Clusters to recruit

C = \left\lceil \frac{n_{\text{recruit}}}{m} \right\rceil, \qquad C_{\text{recruit}} = \left\lceil \frac{C}{1 - \ell} \right\rceil

Per group. Losing a whole cluster loses every participant in it, so an expected loss of clusters inflates the clusters recruited rather than the participants.

Parameters

Expected table $P_{ij}$

Values: A probability per cell, adding to 1 over the table; every row and column above 0

The joint distribution you expect, one row per category of the first variable.

Significance level $\alpha$

Values: Between 0 and 1; usually 0.05

The false-positive rate you accept. A smaller α, for example to allow for several primary outcomes, needs a larger sample.

Power $1-\beta$

Values: Between 0 and 1; usually 0.80 or 0.90

The chance of a significant result if the assumed effect is real. Funders and ethics committees commonly expect at least 0.80.

Expected dropout $q$ Optional

Values: 0% up to, but not including, 100%

The share of participants expected to provide no outcome, from comparable studies. Recruiting more restores the numbers, not the comparability of the groups: if dropout is related to the outcome, the estimate can still be biased.

Intracluster correlation (ICC) $\rho$ Optional

Values: From 0 to 1

For clustered designs only. Published ICCs for health outcomes in primary-care and community clusters are mostly small, often below 0.05 [5]; prefer an estimate from a comparable trial.

Average cluster size $m$ Optional

Values: At least 1

The average number of participants per cluster. Given with the ICC.

Variation in cluster size $\mathrm{CV}$ Optional

Values: 0 or more; 0 for equal clusters

The SD of cluster sizes divided by their mean. Unequal clusters cost power on their own account [3].

Expected loss of whole clusters $\ell$ Optional

Values: 0% up to, but not including, 100%

Clusters, such as practices or schools, expected to withdraw entirely.

Correction for few clusters $F_t$ Optional

Values: On or off

Worth applying when there are few clusters per group, roughly fewer than 15–20.

Assumptions

Participants are independent apart from any clustering specified, each in exactly one cell.
Every cell expects at least 5 participants, so the chi-square approximation holds; the calculator reports the smallest expected count.
With clustering: clusters are independent, with the same ICC throughout, and the analysis accounts for the clustering, for example with a mixed model or cluster-level summaries [2].
Dropout is unrelated to the outcome; recruiting more participants restores the numbers but cannot remove bias.

Worked examples

A 2 × 2 table

Expected cells 0.30, 0.20 / 0.20, 0.30, with margins of one half: w = 0.20 on 1 degree of freedom; α = 0.05, 80% power: N = 197.

Sample size: 197

Protocol statement
To detect an association of w = 0.2 with 80% power at a significance level of 0.05, using a chi-square test of independence on a 2 × 2 table, the required sample size is 197 participants. Calculated with StatWisely (statwisely.com) using: N: solve the noncentral chi-square power at λ=Nw² on (r-1)(c-1) df. These are planning estimates; confirm the final design with a qualified statistician.

Open this example in the calculator

A 2 × 3 table

Expected cells 0.20, 0.15, 0.15 / 0.10, 0.20, 0.20: w = 0.218 on 2 degrees of freedom: N = 203, with at least 30 expected in every cell.

Sample size: 203

Open this example in the calculator

Validation

Every worked example on this page is recomputed by the StatWisely engine whenever the software is built. If a number or formula here stopped matching the calculator, the release would be stopped until this document was corrected.
An independent implementation in R recomputes this test every night and before each production release, and the results are compared with the engine’s.

How to cite

StatWisely. Sample size for a chi-square test of independence. Method document, engine version 1.0.0, reviewed 2026-10-01. https://statwisely.com/methods/proportions-independence/

Cite the original references for the formulas your design uses as well.

References

1.Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Hillsdale, NJ: Lawrence Erlbaum Associates; 1988.
2.Donner A, Klar N. Design and Analysis of Cluster Randomization Trials in Health Research. London: Arnold; 2000.
3.Eldridge SM, Ashby D, Kerry S. Sample size for cluster randomized trials: effect of coefficient of variation of cluster size and analysis method. International Journal of Epidemiology. 2006;35(5):1292–1300. doi:10.1093/ije/dyl129
4.Hayes RJ, Moulton LH. Cluster Randomised Trials. 2nd ed. Boca Raton: CRC Press; 2017.
5.Adams G, Gulliford MC, Ukoumunne OC, Eldridge S, Chinn S, Campbell MJ. Patterns of intra-cluster correlation from primary care research to inform study design and analysis. Journal of Clinical Epidemiology. 2004;57(8):785–794. doi:10.1016/j.jclinepi.2003.12.013