Hypothesis Testing with PythonUSING STATASTICS FOR FINDING SIGNIFICANCE Contributors ¯\_(ツ)_/¯Vivek Chuadhary | Chintan Chitroda|Manvendra Singh

Hey Data Science Enthusiast,again we are back withone of the mini book on applied statistics with some ofthe methods & this is the basic version of the book,verysoon another part will be out.Everyone out there learn statistics but they always failto apply when it comes to solving any project why?Because out of 100% almost 80% to 85% are scared aboutResearch,Statistics & applying your commensence.When it comes to commensence you can not master with anycourse it will be develop by your curiosity & understanding theproblem statement deeply that what makes you get into by takingthis initial step.Before applying statistics you have to think about assumption asper your given problem statement as statistics work onassumption but likelyhood to be true,what it means,alwaysstatistics can't give you the true result & you have to compareparticular result after applying statistics with your strong domainknowledge that you are working with (understanding problemstatement deeply & strongly).In this book we have cover some of the stuff that mayhelp you out to explore & some of the resources havebeen taken from other parts as well to combine it in agood manner whether many of the stuff are custom.We wish you will love this book & we are coming withupdate version for the same in 4 to 5 upcomingversion.Happy Learning

What is Hypothesis Testing and Why we do it ?Hypothesis testing is a statistical method that is used in making statistical decisions using experimental data.Hypothesis Testing is basically an assumption that we make about the population parameter. -- GoogleIn simple words we make a Yes (Significant) or No (Not Significant) decision using Statastics using a sample ofpopulation data to check significance between features.we have to make decisions about the hypothesis. These decisions include deciding if we should accept the nullhypothesis or if we should reject the null hypothesis. Every test in hypothesis testing produces the significancevalue for that particular test. In Hypothesis testing, if the significance value of the test is greater than thepredetermined significance level, then we accept the null hypothesis. If the significance value is less than thepredetermined value, then we should reject the null hypothesis.For example,if we want to see the degree of relationship between two stock prices and the significance value of the correlationcoefficient is greater than the predetermined significance level, then we can accept the null hypothesis andconclude that there was no relationship between the two stock prices. However, due to the chance factor, itshows a relationship between the variables.