Chi-Square Test
A statistical hypothesis testing method that determines whether distributions agree by comparing observed frequencies with theoretical frequencies. The test statistic follows a chi-square distribution, and the method is widely used to test the fairness of randomness.
Timeline (last 90 days)
The author measured one million byte modulo operations, with the first 6 entries landing about 101,000 times and the last 4 about 97,500 times, and a chi-square test p-value of almost zero.
The chi-square test requires expected frequencies in each category to be typically no less than 5, otherwise the approximation fails; in a spinning-wheel scenario the number of spins should be far greater than the number of entries.
Data drift detection originated as a concept in the machine learning field; detection methods include statistical tests (such as the Kolmogorov-Smirnov test and chi-squared test) and distribution distance measures (such as KL divergence and Wasserstein distance).
All Facts (3)
The author measured one million byte modulo operations, with the first 6 entries landing about 101,000 times and the last 4 about 97,500 times, and a chi-square test p-value of almost zero.
50%UnverifiedThe chi-square test requires expected frequencies in each category to be typically no less than 5, otherwise the approximation fails; in a spinning-wheel scenario the number of spins should be far greater than the number of entries.
50%UnverifiedData drift detection originated as a concept in the machine learning field; detection methods include statistical tests (such as the Kolmogorov-Smirnov test and chi-squared test) and distribution distance measures (such as KL divergence and Wasserstein distance).
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