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

#### The hypothesis that chance or random processes alone are responsible for the results is called as _______ hypothesis?

Correct!
Wrong!

#### Rejecting null hypothesis implies that there is an association between two measured entities?

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#### If the data collected as a result of random sampling process does provide strong evidence against null hypothesis, then the null hypothesis is rejected.

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Wrong!

#### If the test scores differ based on the gender group such as male or female or other parameters included in the tests, then the null hypothesis can be rejected.

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#### If the data observed in the sample test is highly unlikely to occur, then the null hypothesis is ________?

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#### Given two samples taken from the same population, a stronger null hypothesis would mean _______?

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#### P-value less than ________ would mean that null hypothesis can be rejected.

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#### In case the estimated value is more than or less than the reference value, which of the following test is appropriate?

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Machine Learning Hypothesis Testing

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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.