Sample Size Determination Explained | How Much Data Do You Actually Need? #SampleSize #Statistics

How much data is enough? This is one of the most important questions in statistics, experimentation, A/B testing, data science, business analytics, market research, and clinical trials. In this video, you'll learn the complete intuition behind Sample Size Determination and why collecting too little—or too much—data can lead to poor decisions. We cover: ✅ What is sample size? 01:38 ✅ Why small samples are dangerous? 02:44 ✅ Precision And Standard Error 03:56 ✅ False Positives vs False Negatives 05:06 ✅ Effect Size and Minimum Detectable Effect (MDE) 06:08 ✅ Power Analysis 07:48 ✅ Business Trade-Offs 09:08 ✅ Common Sample Size Mistakes 09:58 ✅ Summary 11:28 By the end of this video, you'll understand how statisticians, analysts, researchers, product managers, and data scientists decide how much data is needed before making important decisions. 📊 Perfect for: • Data Analysts • Data Scientists • Business Analysts • Product Managers • Researchers • Statistics Students • A/B Testing Practitioners Subscribe to Shital's Data Desk for beginner-friendly explanations of statistics, experimentation, analytics and data science #SampleSize #Statistics #ABTesting #DataScience #expérimentation #PowerAnalysis #StatisticalPower #MDE #HypothesisTesting #ConfidenceIntervals #StandardError #BusinessAnalytics #MachineLearning #ClinicalTrials #ProductAnalytics #ResearchMethods #StatisticalSignificance #DataAnalytics #DataScientist #ShitalsDataDesk