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Wilcoxon Signed-Ranks Test for Matched Pairs01:09

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The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
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Sign Test for Matched Pairs01:17

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The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
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To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
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Multiple Comparison Tests01:13

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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
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Related Experiment Video

Updated: Nov 5, 2025

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

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Soft Bigram distance for names matching.

Mohammed Hadwan1,2,3, Mohammed A Al-Hagery4, Maher Al-Sanabani5

  • 1Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia.

Peerj. Computer Science
|May 13, 2021
PubMed
Summary

A new Soft Bigram Distance (Soft-Bidist) algorithm improves name matching accuracy. This enhanced approach refines the bi-gram distance (BI-DIST) by softening comparisons for better string identification.

Keywords:
BigramApproximate string matchingCharacter n-gramName matching

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Area of Science:

  • Computer Science
  • Computational Linguistics
  • Data Science

Background:

  • Bi-gram distance (BI-DIST) is an efficient method for string comparison with broad applications.
  • Existing BI-DIST methods face challenges in accurately and efficiently measuring string distances, particularly for name identification.
  • A key limitation of BI-DIST is the lack of a defined distance scale between name bigrams.

Purpose of the Study:

  • To design an accurate and efficient algorithm for name matching.
  • To address the limitations of standard bi-gram distance measures.
  • To enhance the performance of name identification through improved string comparison.

Main Methods:

  • Proposing the Soft Bigram Distance (Soft-Bidist) measure, an extension of BI-DIST.
  • Implementing a softened scale of comparison among name bigrams to improve matching.
  • Validating the proposed method using diverse name matching datasets.

Main Results:

  • The Soft-Bidist measure demonstrates superior performance compared to existing algorithms.
  • The enhanced approach shows improved accuracy in name matching tasks.
  • Empirical results confirm the efficiency and effectiveness of Soft-Bidist across different datasets.

Conclusions:

  • Soft-Bidist offers a significant advancement in name matching accuracy and efficiency.
  • The softened comparison scale effectively addresses limitations in traditional BI-DIST.
  • The proposed method is a valuable tool for applications requiring precise name identification.