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Machine Learning (Hypothesis Testing) Interview Questions

This page represents a list of questions which can be used for preparation of machine learning interviews. The following are some of the areas covered in this set of questions:

Null Hypothesis; Another page which explains the concept in decent manner is Null Hypothesis definition and examples, how to state.
P-value; In simple words, p-value represents likelihood (in terms of probability) of sample results occurring if the null hypothesis is assumed to be true. For example, a p-value of 0.03 would mean that given the null hypothesis is true, the probability that results occur in the sample is 0.03 which is very less. Thus, the alternate hypothesis can be true. Thus, one can reject the null hypothesis.
One-tailed vs Two-tailed tests; This is another page on one and two-tailed tests

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Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. I would love to connect with you on Linkedin.
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Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.

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Professor says:

September 29, 2021 at 12:07 pm

I don’t agree with number 5. Having a different test score doesn’t necessarily mean the null can be rejected. It depends on the magnitude of the difference, as well as standard error. A small difference can be due to random noise.