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Understanding P-Value in Hypothesis Testing

Learn about the P-value, a measure of inconsistency between a hypothesized population parameter and sample statistic, and how it informs hypothesis testing decisions. Understand one-tailed and two-tailed tests, interpreting P-values, and making decisions at different significance levels.

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Understanding P-Value in Hypothesis Testing

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  1. Using the P-Value Section 10.3

  2. P-Value (Observed Significance Level) • It’s the measure of the inconsistency between the hypothesized value for a population parameter and the observed sample statistic. • How likely is this sample if the Ho is really true?

  3. Using the P-Value • Reject if • Fail to Reject if

  4. One tailed test (use the test statistic Z) P-value If the Ho is really true, this is the probability of getting this z-score for the given data

  5. Two-Tailed Test P-Value: Sum of both tails We have to double this to get both tails!

  6. Let’s try one!

  7. Let’s try one!

  8. For which of the following P-Values would the null hypothesis be rejected if we’re using a 0.05 significance level? • 0.003 • 0.045 • 0.084 • 0.128 • 3.12 x 10-5

  9. State whether the observed p-value leads to rejection of the null at the given significance level.

  10. Find the p-Value associated with each of the given test statistics (z-scores)

  11. Find the p-Value associated with each of the given test statistics (z-scores)

  12. Homework • Page 546 (2-10) even (11-17, 20, 22, 31)

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