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Questions and Answers
A researcher is testing if the average height of women is different from 5'4". Which type of hypothesis test is most appropriate?
A researcher is testing if the average height of women is different from 5'4". Which type of hypothesis test is most appropriate?
In hypothesis testing, what does the null hypothesis (H0) typically represent?
In hypothesis testing, what does the null hypothesis (H0) typically represent?
What does the p-value represent in hypothesis testing?
What does the p-value represent in hypothesis testing?
A study concludes that a new drug is effective, but in reality, it has no effect. What error has been made?
A study concludes that a new drug is effective, but in reality, it has no effect. What error has been made?
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In hypothesis testing, under what condition do we reject the null hypothesis?
In hypothesis testing, under what condition do we reject the null hypothesis?
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Flashcards
Hypothesis Testing
Hypothesis Testing
A method to determine if a statement about a population is true or false based on a sample.
Null Hypothesis (H0)
Null Hypothesis (H0)
A statement about a population parameter that we try to disprove.
Type I Error
Type I Error
Rejecting the null hypothesis when it is actually true (False Positive).
P-value
P-value
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Two-Tailed Test
Two-Tailed Test
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Study Notes
Hypothesis Testing
- Hypothesis testing is a method to determine if a statement about a population is true or false based on a sample.
- Null Hypothesis (H0) is a statement about a population parameter that we are trying to disprove.
- Alternative Hypothesis (HA) is a statement that contradicts the null hypothesis.
- The goal is to either reject or fail to reject the null hypothesis, not to accept the alternative hypothesis.
- Two-Tailed Test: Both tails of the distribution are considered, indicating that the alternative hypothesis is about the population parameter being different from a specific value (not equals to).
- One-Tailed Test: Only one tail of the distribution is considered, indicating that the alternative hypothesis is about the population parameter being greater than or less than a specific value.
- Type I Error: Rejecting the null hypothesis when it is actually true (False Positive).
- Type II Error: Failing to reject the null hypothesis when it is actually false (False Negative).
- Alpha (α) is the significance level. It represents the probability of making a Type I error (usually .01, .05, or .10).
- P-value: The probability of obtaining results as extreme as the observed results, assuming the null hypothesis is true.
- A lower p-value indicates stronger evidence against the null hypothesis, increasing the likelihood of rejecting it.
- If the p-value is less than or equal to the significance level (alpha), we reject the null hypothesis. If it is greater than alpha, we fail to reject.
- When the sample standard deviation is unknown, we use the sample standard deviation (s) as an estimate.
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Description
Explore the foundational concepts of hypothesis testing in statistics. This quiz covers key terms such as null and alternative hypotheses, as well as types of tests and errors. Test your understanding of one-tailed and two-tailed tests, and the implications of Type I and Type II errors.
