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Statistics: The Art and Science of Learning from Data

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  • Dr. Olivier Gatete

    (Mathematics and ICT Senior Lecturer, Texila American University)

Abstract

Statistics is a discipline that bridges the art of interpretation and the science of data analysis. It provides the tools and methodologies to extract meaningful insights from data, enabling informed decision-making across diverse fields such as healthcare, economics, social sciences, and technology. This article explores the dual nature of statistics as both an art and a science, emphasizing its role in transforming raw data into actionable knowledge. It discusses key statistical concepts, the importance of statistical literacy, and the challenges and opportunities in the era of big data. By understanding the interplay between creativity and rigor in statistical practice, we can better appreciate its significance in shaping our understanding of the world.

Suggested Citation

  • Dr. Olivier Gatete, 2025. "Statistics: The Art and Science of Learning from Data," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(7), pages 285-301, July.
    • Handle: RePEc:bcp:journl:v:9:y:2025:issue-7:p:285-301
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    References listed on IDEAS

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    1. Ronald L. Wasserstein & Allen L. Schirm & Nicole A. Lazar, 2019. "Moving to a World Beyond “p," The American Statistician, Taylor & Francis Journals, vol. 73(S1), pages 1-19, March.
    2. Francine D. Blau & Lawrence M. Kahn, 2017. "The Gender Wage Gap: Extent, Trends, and Explanations," Journal of Economic Literature, American Economic Association, vol. 55(3), pages 789-865, September.
    3. Dr. Olivier Gatete, 2025. "Advancing Predictive Analytics: Integrating Machine Learning and Data Modelling for Enhanced Decision-Making," International Journal of Latest Technology in Engineering, Management & Applied Science, International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS), vol. 14(4), pages 169-189, April.
    4. Ronald L. Wasserstein & Nicole A. Lazar, 2016. "The ASA's Statement on p -Values: Context, Process, and Purpose," The American Statistician, Taylor & Francis Journals, vol. 70(2), pages 129-133, May.
    5. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, January.
    Full references (including those not matched with items on IDEAS)

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