A commonly used non parametric statistical test method used to compare the distribution of categorical variables at different levels for differences. It is usually applied to repeated measurement data, where each individual or process is tested or observed multiple times under different conditions.

    In practical applications, Cochran's Q-test is a non parametric statistical method used to compare the distribution differences between multiple classification levels. It is suitable for repeated measurement data and allows analysis of non normal or small sample data to determine whether there are significant differences between classification levels.

    Data description:

    The analysis results are as follows:

    According to frequency analysis, there are 6 out of A1, accounting for 7.41%; There are 11 in A1, accounting for 13.58%; There are 44 in A1, accounting for 54.32%; There are 20 in A1, accounting for 24.69%.

    There are 14 out of A2, accounting for 17.28%; There are 10 out of A2, accounting for 12.35%; There are 37 out of A2, accounting for 45.68%; There are 20 out of A2, accounting for 24.69%.

    The results showed that the result statistic of the Q-test was -0.12, with a p-value of 1.0>0.05, indicating that there were no significant differences among different variables overall.

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