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Questions and Answers
What is the purpose of hypothesis testing?
What is the purpose of hypothesis testing?
- To determine if the observed data are consistent with the hypothesis (correct)
- To prove the null hypothesis is true
- To calculate the p-value accurately
- To reject all hypotheses
What does the p-value measure in hypothesis testing?
What does the p-value measure in hypothesis testing?
- It quantifies how likely the observed data are under the assumption of the null hypothesis (correct)
- It measures the probability of the null hypothesis being correct
- It confirms the alternative hypothesis
- It proves the null hypothesis
In statistical hypothesis testing, what does a p-value close to 1 indicate?
In statistical hypothesis testing, what does a p-value close to 1 indicate?
- Strong evidence against the null hypothesis
- The null hypothesis is likely true (correct)
- Strong evidence for the null hypothesis
- The null hypothesis is definitely false
What is the interpretation of a p-value of 0.125 in hypothesis testing?
What is the interpretation of a p-value of 0.125 in hypothesis testing?
How does statistical hypothesis testing use the concept of p-value?
How does statistical hypothesis testing use the concept of p-value?
Why should one not conclude that a p-value of 1 means the null hypothesis is true?
Why should one not conclude that a p-value of 1 means the null hypothesis is true?
What does the p-value measure in hypothesis testing?
What does the p-value measure in hypothesis testing?
Why is it incorrect to say we have found evidence for the null hypothesis?
Why is it incorrect to say we have found evidence for the null hypothesis?
What does a small p-value indicate in hypothesis testing?
What does a small p-value indicate in hypothesis testing?
Why is it emphasized that we should not discuss 'evidence for H0'?
Why is it emphasized that we should not discuss 'evidence for H0'?
What role does sample size play in determining the p-value?
What role does sample size play in determining the p-value?
What distinguishes statistical significance from practical significance in hypothesis testing?
What distinguishes statistical significance from practical significance in hypothesis testing?
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