Chi Square Calculator

Calculate chi-square statistic and p-value for goodness-of-fit and independence tests from observed and expected frequencies.

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Test type

Chi-Square Test

χ² = 2

The chi-square goodness-of-fit test yields χ² = 2 with 5 degrees of freedom (p = 0.849145). The result is not statistically significant at α = 0.05, so we fail to reject the null hypothesis.

Chi-Square Statistic
2
P-Value
0.849145
Degrees of Freedom
5
Significant at α = 0.05
No
Significant at α = 0.01
No

Interpretation

The p-value of 0.849145 is above 0.05, suggesting insufficient evidence to reject the null hypothesis at the 5% significance level. The observed frequencies are consistent with the expected frequencies.

Also in Statistics

Hypothesis Testing

Chi-square calculator: goodness-of-fit and independence tests with p-values

A chi-square calculator computes the chi-square statistic, degrees of freedom, and p-value for goodness-of-fit and independence tests. Enter observed and expected frequencies for a goodness-of-fit test, or a contingency table for an independence test, to determine whether observed data differs significantly from expected values.

Goodness-of-fit vs. independence tests

A goodness-of-fit test compares observed frequencies to expected frequencies for a single categorical variable. It answers whether the observed distribution matches a theoretical one. Degrees of freedom equal the number of categories minus one.

An independence test determines whether two categorical variables are associated. It uses a contingency table of observed counts and computes expected counts from row and column totals. Degrees of freedom equal (rows - 1) times (columns - 1).

χ² = Σ (Oᵢ − Eᵢ)² / Eᵢ

Chi-square statistic: sum of squared differences between observed and expected, divided by expected.

Frequently asked questions

What are the assumptions of a chi-square test?

Observations must be independent, categories mutually exclusive, and expected frequencies should generally be 5 or more in each cell. Small expected values can inflate the chi-square statistic unreliably.

Can chi-square tests be one-tailed?

Chi-square tests are inherently right-tailed because the statistic measures deviation from expected values, and larger values indicate greater departure from the null hypothesis.

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