IDEAS home Printed from https://ideas.repec.org/a/adp/jbboaj/v9y2019i2p41-44.html
   My bibliography  Save this article

Modern State of Statistical Hypotheses Testing and Perspectives of its Development

Author

Listed:
  • Kachiashvili KJ

    (Faculty of Informatics and Control Systems, Georgian Technical University, Georgia
    Vekua Institute of Applied Mathematics of the Tbilisi State Un)

Abstract

A statistical hypothesis is a formalized record of properties of the investigated phenomenon and relevant assumptions. The statistical hypotheses are set when random factors affect the investigated phenomena, i.e. when the observation results of the investigated phenomena are random. The properties of the investigated phenomenon are completely defined by its probability distribution law. Therefore, the statistical hypothesis is an assumption concerning this or that property of the probability distribution law of a random variable. Mathematical statistics is the set of the methods for studying the events caused by random variability and estimates the measures (the probabilities) of possibility of occurrence of these events. For this reason, it uses distribution laws as a rule. Practically all methods of mathematical statistics one way or another, in different doses, use hypotheses testing techniques. Therefore, it is very difficult to overestimate the meaning of the methods of statistical hypotheses testing in the theory and practice of mathematical statistics.

Suggested Citation

  • Kachiashvili KJ, 2019. "Modern State of Statistical Hypotheses Testing and Perspectives of its Development," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 9(2), pages 41-44, March.
    • Handle: RePEc:adp:jbboaj:v:9:y:2019:i:2:p:41-44
    DOI: 10.19080/BBOAJ.2019.09.555759
    as

    Download full text from publisher

    File URL: https://juniperpublishers.com/bboaj/pdf/BBOAJ.MS.ID.555759.pdf
    Download Restriction: no
    File URL: https://juniperpublishers.com/bboaj/BBOAJ.MS.ID.555759.php
    Download Restriction: no
    File URL: https://libkey.io/10.19080/BBOAJ.2019.09.555759?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Bansal, Naveen K. & Miescke, Klaus J., 2013. "A Bayesian decision theoretic approach to directional multiple hypotheses problems," Journal of Multivariate Analysis, Elsevier, vol. 120(C), pages 205-215.
    2. KJ Kachiashvili, 2018. "On One Aspect of Constrained Bayesian Method for Testing Directional Hypotheses," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 2(5), pages 2901-2903, March.
    3. Naveen K. Bansal & Gholamhossein G. Hamedani & Mehdi Maadooliat, 2016. "Testing multiple hypotheses with skewed alternatives," Biometrics, The International Biometric Society, vol. 72(2), pages 494-502, June.
    4. K. J. Kachiashvili & D. I. Melikdzhanian, 2006. "Identification Of River Water Excessive Pollution Sources," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 5(02), pages 397-417.
    5. Kartlos Joseph Kachiashvili & Archil Iveri Prangishvili, 2018. "Verification in biometric systems: problems and modern methods of their solution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(1), pages 43-62, January.
    6. K. J. Kachiashvili, 2003. "Generalization Of Bayesian Rule Of Many Simple Hypotheses Testing," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 2(01), pages 41-70.
    7. Kartlos J. Kachiashvili & Muntazim A. Hashmi & Abdul Mueed, 2012. "The Statistical Risk Analysis As The Basis Of The Sustainable Development," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 9(03), pages 1-10.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. K. J. Kachiashvili & I.A. Prangishvili & J. K. Kachiashvili, 2019. "Constrained Bayesian Methods for Testing Directional Hypotheses Restricted False Discovery Rates," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 9(3), pages 47-56, March.
    2. KJ Kachiashvili, 2018. "On One Aspect of Constrained Bayesian Method for Testing Directional Hypotheses," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 2(5), pages 2901-2903, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:adp:jbboaj:v:9:y:2019:i:2:p:41-44. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Robert Thomas (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.