Sampling in Research | Sample Size, Error, Standard Error, Confidence & Power

This lecture provides a systematic and conceptually rigorous explanation of sampling in research, focusing on key statistical concepts essential for educational research, social sciences, and behavioral studies. The video is designed to strengthen foundational understanding while supporting examination and research readiness. The lecture begins with an overview of sampling and the factors influencing sampling, including population characteristics, research objectives, variability, resources, and methodological constraints. It then examines sample size, explaining its importance in relation to accuracy, reliability, statistical power, and generalizability of findings. A detailed discussion follows on sampling error, clarifying how differences between sample statistics and population parameters arise. The concepts of standard error (SE) and standard error of the mean (SEM) are explained to show how sampling variability is quantified and interpreted. The video further explains confidence and the 95% rule, illustrating how confidence intervals are constructed and interpreted in inferential statistics. The role of statistical power in hypothesis testing is discussed, emphasizing its relationship with sample size, effect size, and significance level. The lecture concludes with an examination of sample representativeness, highlighting its importance for external validity and meaningful generalization of research findings. This video is especially useful for B.Ed., M.Ed., Ph.D. coursework, UGC NET/SET (Education), research methodology papers, and competitive examinations, as well as for researchers seeking conceptual clarity in sampling and inference. #Sampling #SamplingInResearch #ResearchMethodology #EducationalResearch #SampleSize #SamplingError #StandardError #StandardErrorOfMean #ConfidenceIntervals #95PercentRule #StatisticalPower #InferentialStatistics #ResearchDesign #QuantitativeResearch #StatisticsForResearch #UGCNET #UGCNETEducation #UGCNETStatistics #BEd #MEd #PhDCoursework