How to perform Chi-Squared test using SciPy?

The Chi-Squared test is commonly used to test hypotheses about the distribution of categorical data. We’ll show how to perform a Chi-Squared test in Python using the SciPy library.

python scipy chi squared test

Chi-square calculations

We’ll demonstrate how to conduct a chi-squared test for a given set of observed frequencies using the SciPy library. We’ll calculate the chi-squared test using the SciPy library, as it provides the built-in chisquare function for this purpose.

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The chisquare function returns a p-value. Setting a significance level of 5% helps us determine if the observed deviations from expected frequencies are statistically significant.

from scipy import stats

numbers = [45, 67, 33, 54, 33]

chi = stats.chisquare(numbers)

print(f"pvalue equals {round(chi.pvalue, 4)}")
if chi.pvalue < 0.05:
    print("Hypothesis can be rejected")
else:
    print("Hypothesis is valid")

We compare the p-value against a significance level of 0.05. If the p-value is less than 0.05, the null hypothesis can be rejected, meaning the deviations are statistically significant.

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This indicates that the deviations between the observed frequencies are statistically significant, allowing us to reject the null hypothesis.