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Abstract

This article examines the applications and benefits of inferential statistical methods in Library and Information Science (LIS) research. While descriptive statistics provide a summary of data, inferential methods allow researchers to draw meaningful conclusions and make predictions based on sample data. In the context of LIS, inferential statistics find numerous applications, including user behavior analysis, evaluation of information services, collection assessment, and predictive modeling. By utilizing inferential techniques, researchers can go beyond descriptive analysis, generalize findings, and gain deeper insights into the phenomena under investigation. The adoption of inferential statistical methods in LIS research empowers researchers to make evidence-based decisions, predict user behavior, evaluate the impact of services, and contribute to the growth of cumulative knowledge within the field. However, researchers must consider challenges related to appropriate test selection, data quality, and addressing assumptions to ensure accurate and reliable results. Exploring and applying inferential statistical methods in LIS research will advance the field, enable evidence-based practices, and strengthen the knowledge base in Library and Information Science.