Hypothesis Testing in Data Science

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Questions and Answers

In hypothesis testing, what does the null hypothesis typically state?

  • The model predicts better than the null model; or an old model
  • The two populations have similar means and variances
  • There is no difference between the populations based on their samples (correct)
  • The variable affects the outcome; its coefficient is not zero

When comparing two means in hypothesis testing, which assumption is typically made about the populations?

  • They are normally distributed with the same variance (correct)
  • They have significantly different means and variances
  • They are not necessarily normally distributed
  • They are normally distributed with different variances

What does the alternate hypothesis (H1) signify in hypothesis testing?

  • The model predicts better than the null model; or an old model
  • The variable affects the outcome; its coefficient is not zero (correct)
  • The variable does not affect the outcome; its coefficient is zero
  • The two populations have similar means and variances

What does model deployment aim to assess in the context of predictive modeling?

<p>Whether predictions make a difference in preventing customer churn (A)</p> Signup and view all the answers

What does hypothesis testing seek to determine about two populations?

<p>Whether there is a difference between their samples' mean values (D)</p> Signup and view all the answers

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Study Notes

Hypothesis Testing

  • The null hypothesis typically states that there is no significant difference or relationship between variables.
  • When comparing two means, it is typically assumed that the populations have equal variances.

Alternate Hypothesis (H1)

  • The alternate hypothesis (H1) signifies that there is a significant difference or relationship between variables.

Model Deployment

  • Model deployment aims to assess whether the model generalizes well to new, unseen data in the context of predictive modeling.

Purpose of Hypothesis Testing

  • Hypothesis testing seeks to determine whether the observed difference between two populations is due to chance or if it is a real effect.

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