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Using standardization for fair data evaluation

机译:使用标准化进行公平数据评估

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摘要

Evaluation on a specific criteria is divided into quantitative evaluation and qualitative evaluation, the former can clearly depict scores in objective manner while the latter is obscure to measure. To reduce the obscurity, we used formula of Z-score and T-score through `standardization of normal distribution'. To testify the effect, we set a simulation of two groups of which consisted 3 interviewers and 5 interviewees. The result after using the method lead to equal mean and equal standard deviation regardless of different interviewers. Also, by using T-score, we again rearranged scores once again by using T-score, in order to enable comparing between criteria possible. By doing so, we summed up the altered scores, and arranged the ranks of interviewees from the highest to the lowest. This proved to be effective on reducing obscurity compared to raw scores' sum up.
机译:对特定标准的评估分为定量评估和定性评估,前者可以客观地清晰地描述得分,而后者则难以衡量。为了降低模糊性,我们通过“正态分布的标准化”使用Z得分和T得分的公式。为了证明这种效果,我们对两组进行了模拟,其中包括3名访问者和5名访问者。使用该方法后的结果将导致均值和标准偏差均等,而与不同的访问者无关。另外,通过使用T分数,我们再次使用T分数再次重新排列了分数,以便能够在可能的标准之间进行比较。通过这样做,我们总结了改变的分数,并按照从高到低的顺序排列了受访者的排名。与原始分数的总和相比,这被证明在减少模糊性方面是有效的。

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