ML with Python : Zero to Hero | Video 13 | Testing of Hypothesis | Venkat Reddy AI Classes

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In this comprehensive tutorial on hypothesis testing, we dive deep into the fundamental concepts and steps involved in statistical hypothesis testing. We begin by explaining what hypothesis testing is and its significance in making data-driven decisions.

The tutorial covers the concept of the null hypothesis, which is a default assumption that there is no significant effect or difference in a given situation. We then introduce the test statistic, a standardized value used to determine the probability of observing the test results under the null hypothesis.

Next, we explain the P-value, which helps in deciding whether to reject the null hypothesis. A lower P-value indicates stronger evidence against the null hypothesis, leading to its rejection. Understanding these concepts is crucial for anyone involved in data analysis, research, or any field that relies on statistical testing to make informed conclusions.

This tutorial is ideal for students, researchers, and professionals looking to strengthen their understanding of hypothesis testing and improve their data analysis skills.

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[hovseppoghosyan]

Thank you for explaining everything so clearly. I hope this column will continue and you will explain other types of tests.

[Manisha-iubc]

Hi sir, your video is very helpful for me, because I am learning ML from your videos sir please upload the next part of this series I am eagerly waiting for the next part.. thank you so much for giving such knowledge contact ❤