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Questions and Answers
In hypothesis testing, what does the null hypothesis typically state?
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?
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?
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?
What does model deployment aim to assess in the context of predictive modeling?
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What does hypothesis testing seek to determine about two populations?
What does hypothesis testing seek to determine about two populations?
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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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Test your knowledge of hypothesis testing in data science, including model building, evaluation, and deployment, as well as the prediction of outcomes based on available inputs.
