Hypothesis Testing

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What is Hypothesis Testing?

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Hypothesis Testing is a statistical method used to make decisions about the validity of a claim based on sample data. It involves formulating a null hypothesis (H0) and an alternative hypothesis (Ha), then using statistical tests to determine whether to reject H0.

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What is a 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. It is what the test aims to provide evidence against.

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What is an Alternative Hypothesis (Ha)?

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The Alternative Hypothesis (Ha) is a statement that indicates the presence of an effect or a difference. It is what researchers aim to support through their data.

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What does a P-value represent?

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A P-value represents the probability of obtaining the observed results, or more extreme results, if the null hypothesis is true. It helps determine the strength of the evidence against H0.

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What is the significance level (α)?

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The significance level (α) is a threshold set by the researcher to determine when to reject the null hypothesis. Common values are 0.05, 0.01, and 0.10.

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What does it mean to reject the null hypothesis?

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Rejecting the null hypothesis means that the sample data provides sufficient evidence to support the alternative hypothesis, indicating that an effect or difference likely exists.

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What does it mean to fail to reject the null hypothesis?

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Failing to reject the null hypothesis means that there is not enough evidence to support the alternative hypothesis, and the null hypothesis remains a plausible explanation.

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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. It is also known as a false positive.

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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. It is also known as a false negative.

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How do you state a hypothesis test for a proportion?

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To state a hypothesis test for a proportion, define the null hypothesis (H0: p = p0) and the alternative hypothesis (Ha: p ≠ p0, p < p0, or p > p0), where p0 is the hypothesized population proportion.