Description: The Uniformity Test is a statistical technique used to determine whether a sample of data comes from a uniform distribution. This means that it seeks to verify whether all values in the sample range have the same probability of occurring. Uniformity is a fundamental concept in statistics, as many tests and models assume that data is uniformly distributed. The test is based on comparing the observed frequency of the data with the expected frequency under the hypothesis of uniformity. If the differences are significant, one can reject the hypothesis that the data is uniform. There are different methods to carry out this test, such as the Chi-square test, the Kolmogorov-Smirnov test, and the Anderson-Darling test, each with its own characteristics and applications. The Uniformity Test is essential in various fields, including scientific research, product quality, and data analysis, as it allows researchers and analysts to validate assumptions about the distribution of their data before applying more complex statistical models.
