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It can pass the K-S test under the 0.05 significance level.
The results of K-S test are shown in Table 5.
The null hypothesis cannot be rejected when the hypothesized distribution passes the K-S test [31].
To compare distributions and SSV, we have performed the two-sample K-S test [35].
Open image in new window Fig. 1 Spring/autumn rainfall anomalies K-S test, sig = 0.9.
The appropriate models were selected using a methodology based on K-S test and subsequent field validation.
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Therefore, we applied the K-S tost to compare P-values for two-component versus three-component mixtures.
In this study, the normality of variables was checked by one-sample K S test.
The K S test statistic of fitting these distributions is presented in Table 3.
K S test revealed that the data were not normally distributed.
The K S test result for quantifying goodness of GEV fitting is provided as well.
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