Inferential Statistics Explained Simply | Sampling, CLT, CI, p-value & Test #DataScienceForBeginners

Inferential Statistics is one of the most important foundations of data analysis — and in this video, we break it down in the simplest, clearest, and most practical way. Whether you're learning data science, analytics, statistics for business, or preparing for interviews, this video will help you understand how we use small samples to make big decisions about large populations. 🎓 In This Video, You’ll Learn: ✔ What is Inferential Statistics? ✔ Estimation: Using samples to estimate population values ✔ Sampling Methods (Random, Systematic, Stratified, Cluster) ✔ Central Limit Theorem (CLT) – the engine behind inference ✔ Confidence Intervals explained simply ✔ Hypothesis Testing (Null vs Alternative) ✔ p-values and significance levels (α = 0.05) ✔ Choosing the right statistical test ✔ Common mistakes analysts make ✔ Real-World Examples (Medicine, Marketing, Surveys, Product Testing) 🧠 Who Is This Video For? Beginners in: Data Analytics Data Science Statistics Students Business/Marketing Analysts Anyone wanting to understand statistical decision-making 📢 Subscribe for More Beginner-Friendly Videos Data, Analytics, Excel, Power BI, Statistics & Career Tips 🔔 Subscribe to Shital’s Data Desk #InferentialStatistics #StatisticsForBeginners #DataAnalysis #SamplingMethods #CentralLimitTheorem #ConfidenceInterval #HypothesisTesting #pvalue #SignificanceLevel #DataScienceBasics #LearnStatistics #DataAnalytics #ABTesting #ShitalsDataDesk