Hypothesis Testing Made Clear | Concepts, Assumptions, Errors, and Real Applications| CS1 HT Part1 скачать в хорошем качестве

Hypothesis Testing Made Clear | Concepts, Assumptions, Errors, and Real Applications| CS1 HT Part1

Hypothesis Testing Made Clear | Concepts, Assumptions, Errors, and Real Applications| CS1 HT Part1 Linkedin   / pratap-padhi   Website https://smearseducation.com/ Join my FREE Skool Community to get all updates and support https://www.skool.com/sme-education-9... Watch my previous recordinds on CS2 Time Series 👉    • Master Time Series Forecasting:Guide to AR...   CS2 Risk Modelling and Survival Analysis 👉   • Why One Random Variable Is Not Enough for ...   👉    • What is a Stochastic Process? Easy explana...   CS1 Previous recorded videos watch 👉    • What are discrete random variables? |Class...   CM1 Previous recorded videos watch 👉    • How to calculate simple interest | Fundame...   👉    • CM1 Y Part2 Class1- A beginner's introduct...   0:00 Introduction to Hypothesis Testing 0:13 Difference between scientific, logical, and statistical testing 0:22 What kinds of questions hypothesis testing can answer 0:41 What you can and cannot prove statistically 1:07 Overview of what you will learn 1:25 Structure of the next two classes 1:37 Meaning of population and sample 2:05 One population vs two populations 2:45 What statistical inference really means 3:17 Testing population mean using sample data 3:58 Key parameter in hypothesis testing, mu 4:50 Assumptions about population distribution 6:30 Normal, Binomial, and Poisson frameworks 7:20 Parametric vs nonparametric testing 8:34 Z test and t test overview 10:00 Chi square and F tests introduction 12:44 Example of one population hypothesis test 14:32 Formulating null and alternative hypothesis 15:50 Understanding acceptance and rejection regions 17:17 Level of significance explained 19:06 Concept of test statistic 22:41 Central Limit Theorem connection 25:03 Critical value from Z table 27:49 Decision making using test statistic 31:30 Why we reject extreme values 33:04 Practical meaning of level of significance 37:10 How changing LS changes conclusion 40:10 Introduction to p-value concept 42:05 P-value interpretation 45:00 Practical example using p-value 48:22 Hypothesis testing when sigma is unknown 51:40 Using t distribution 56:48 Testing variance using chi square 1:04:20 Two-sided chi square test example 1:12:05 Summary of what was completed 1:16:20 Preview of next class topics 1:19:00 Type I and Type II errors explained 1:24:50 Practical intuition for errors DESCRIPTION This class gives a full conceptual foundation of hypothesis testing from the ground up. The session starts with the core question. What is hypothesis testing and why do we need it. The difference between scientific experiments, logical arguments, and statistical experiments is explained in simple language. You learn the meaning of population, sample, and statistical inference. Clear examples show how conclusions about large populations are made using small samples. The idea of one population and two population problems is explained with real life cases. The class builds step by step. First, you learn how to test a population mean when the population standard deviation is known using the Z test. The logic of null hypothesis, alternative hypothesis, level of significance, and critical region is shown clearly. Central Limit Theorem is connected to hypothesis testing so you understand why the test statistic follows a normal distribution. The concept of acceptance and rejection region is explained using diagrams and intuition. Then the session introduces the p-value approach. You learn how to compute p-value, how to interpret it, and how to take decisions without using fixed critical values. The difference between LS method and p-value method is made very clear. Next, the class moves to the situation where population standard deviation is unknown. Here the t-test is introduced. A full numerical example shows how to compute sample mean, sample standard deviation, test statistic, and final conclusion. After mean testing, the class explains how to test population variance using the chi square distribution. A complete step by step example demonstrates how to frame the hypothesis, compute chi square statistic, and make decisions in a two sided test. Finally, the most important conceptual topic is covered. Type I error and Type II error. Practical stories and analogies explain what it means to wrongly reject a true hypothesis and wrongly accept a false hypothesis. By the end of the session you clearly understand: how hypothesis testing works what assumptions are needed how to calculate test statistics how to use Z, t, and chi square tests how to interpret p-values how decisions depend on level of significance why statistical decisions always involve risk