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Library; Behavior analysis; Inferential techniques; Generalize; Collection assessment

Introduction

In the field of Library and Information Science (LIS), data analysis plays a crucial role in understanding user behavior, evaluating the impact of services, and making evidence-based decisions [1]. While descriptive statistics provide a summary of data, inferential statistical methods offer powerful tools for drawing meaningful conclusions and making predictions based on sample data [2]. This article explores the applications and benefits of inferential statistical methods in the context of LIS research, highlighting their potential to enhance knowledge and inform decision-making. Inferential statistical methods enable researchers to generalize findings from a sample to a larger population. They provide a framework for drawing conclusions, making predictions, and assessing the significance of relationships within the data. By employing inferential techniques, researchers can go beyond mere description and gain deeper insights into the phenomena under investigation [3].

Applications in LIS research

User behavior analysis: Inferential statistical methods can be applied to analyze user behavior patterns in library and information systems. For example, through techniques such as hypothesis testing and regression analysis, researchers can determine the factors influencing user engagement, satisfaction, and information-seeking behavior [4].

Evaluation of information services: Inferential statistics help evaluate the impact and effectiveness of information services offered by libraries. By conducting experiments or utilizing quasi-experimental designs, researchers can assess the causal relationships between service interventions and user outcomes, such as learning outcomes, information literacy skills, or resource usage [5].

Collection assessment: Inferential statistical methods aid in assessing the quality and relevance of library collections. Through techniques like sampling and confidence intervals, researchers can estimate the representativeness of a collection, identify gaps, and determine the sufficiency of resources to meet users’ information needs [6].

Predictive modeling: Inferential statistical methods can be utilized to build predictive models in LIS research. Researchers can employ techniques like regression analysis and time series analysis to forecast future trends, predict user behavior, or estimate demand for library services and resources [7].

Benefits and Challenges

The use of inferential statistical methods in LIS research offers several benefits [8]. It enables researchers to make evidence-based decisions, provides a foundation for generalization, and facilitates comparisons between different user groups or time periods [9]. Additionally, inferential techniques contribute to the growth of cumulative knowledge within the field. However, challenges such as selecting appropriate statistical tests, ensuring data quality, and addressing assumptions associated with inferential methods should be carefully considered to ensure accurate and reliable results [10].

Conclusion

Inferential statistical methods provide a valuable toolkit for researchers in Library and Information Science, allowing them to move beyond descriptive analysis and uncover deeper insights from data. By applying these methods, researchers can make informed decisions, predict user behavior, evaluate the impact of services, and enhance the quality of library collections. The adoption of inferential statistical techniques empowers LIS professionals to utilize evidence-based practices and contribute to the advancement of the field. Continued exploration and application of inferential statistical methods will further strengthen the knowledge base in Library and Information Science and ultimately benefit both researchers and practitioners in the field. the number of the investigated journals in library and information science to gain a larger picture of the application of statistical methods in the field and conducting a temporal analysis on the interaction between statistical methods and their application areas.

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