Basic Facts About Hypothesis Testing

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What is a null hypothesis?

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A null hypothesis 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 an alternative hypothesis?

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An alternative hypothesis is a statement that indicates the presence of an effect or a difference, opposing the null hypothesis.

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What does a p-value represent in hypothesis testing?

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A p-value represents the probability of observing the data, or something more extreme, given that the null hypothesis is true.

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What is the significance level (alpha) in hypothesis testing?

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The significance level (alpha) is the threshold for determining whether to reject the null hypothesis, commonly set at 0.05.

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What decision is made if the p-value is less than the significance level?

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If the p-value is less than the significance level, the null hypothesis is rejected.

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What is a Type I error?

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A Type I error occurs when the null hypothesis is 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.

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What is the relationship between sample size and p-value?

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Larger sample sizes tend to produce more reliable estimates and can lead to smaller p-values, making it easier to detect an effect.

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What is a confidence interval?

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A confidence interval is a range of values that is likely to contain the population parameter with a specified level of confidence, often 95%.

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How does one interpret a significance level of 0.05?

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A significance level of 0.05 indicates that there is a 5% risk of concluding that a difference exists when there is no actual difference.