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Neutrosophic Approach to Z-Testing for Poisson Count Data

Author

Listed:
  • Muhammad Aslam

    (King Abdulaziz University)

  • Abdulrahman AlAita

    (Damascus University)

Abstract

This paper introduces a novel approach to z-testing for counts that follow the Poisson distribution, particularly when the significance level and sample size are uncertain. The proposed method extends the traditional z-test for Poisson-distributed counts within the framework of neutrosophic statistics. We outline the test statistic and provide a step-by-step guide for its application using a practical example. Our analysis demonstrates that the z-test for Poisson counts under neutrosophic statistics offers greater efficiency, flexibility, and information compared to its classical counterpart.

Suggested Citation

  • Muhammad Aslam & Abdulrahman AlAita, 2025. "Neutrosophic Approach to Z-Testing for Poisson Count Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 87(1), pages 129-146, May.
    • Handle: RePEc:spr:sankhb:v:87:y:2025:i:1:d:10.1007_s13571-024-00349-5
    DOI: 10.1007/s13571-024-00349-5
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    References listed on IDEAS

    as
    1. Tingqing Ye & Baoding Liu, 2022. "Uncertain hypothesis test with application to uncertain regression analysis," Fuzzy Optimization and Decision Making, Springer, vol. 21(2), pages 157-174, June.
    2. Guido W. Imbens, 2021. "Statistical Significance, p-Values, and the Reporting of Uncertainty," Journal of Economic Perspectives, American Economic Association, vol. 35(3), pages 157-174, Summer.
    3. Muhammad Aslam, 2022. "Neutrosophic F-Test for Two Counts of Data from the Poisson Distribution with Application in Climatology," Stats, MDPI, vol. 5(3), pages 1-11, August.
    4. Boris Aleksandrov & Christian H. Weiß & Carsten Jentsch, 2022. "Goodness‐of‐fit tests for Poisson count time series based on the Stein–Chen identity," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 76(1), pages 35-64, February.
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