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Abstrak
Fisher information, a key concept in statistical theory, provides a measure of the amount of information that an observable random variable carries about an unknown parameter. In the context of ordinal statistics, Fisher information plays a crucial role in understanding the efficiency of parameter estimation. Ordinal data, which is categorized into ordered levels or ranks, presents unique challenges when it comes to statistical analysis, particularly in estimating parameters and evaluating the quality of statistical inference. This article explores Fisher information in the context of ordinal statistics, including methods for calculating Fisher information, results from recent studies, and an overview of relevant literature.