Hypothesis Tests

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What is a hypothesis test?

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A hypothesis test is a statistical method used to make decisions about a population based on sample data. It involves formulating a null hypothesis (H0) and an alternative hypothesis (H1) and determining whether to reject H0 based on sample evidence.

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What is the null hypothesis (H0)?

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The null hypothesis (H0) is a statement that there is no effect or no difference, and it serves as the default assumption in hypothesis testing.

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What is the alternative hypothesis (H1)?

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The alternative hypothesis (H1) is a statement that indicates the presence of an effect or a difference, opposing the null hypothesis.

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What are degrees of freedom in hypothesis testing?

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Degrees of freedom refer to the number of independent values or quantities that can vary in an analysis without violating any constraints. In hypothesis testing, it often relates to the sample size.

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What does a p-value represent?

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A p-value represents the probability of obtaining results at least as extreme as the observed results, assuming that the null hypothesis is true.

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What does a smaller p-value indicate?

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A smaller p-value indicates stronger evidence against the null hypothesis, suggesting that it may be rejected.

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What is the significance level (alpha)?

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The significance level (alpha) is the threshold for determining whether a p-value indicates a statistically significant result, commonly set at 0.05.

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What does it mean if p-value < 0.05?

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If p-value < 0.05, it suggests that there is a statistically significant difference, leading to the rejection of the null hypothesis.

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What is a Type I error?

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A Type I error occurs when the null hypothesis is incorrectly rejected when it is actually true.

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What is a Type II error?

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A Type II error occurs when the null hypothesis is not rejected when it is actually false.