Null Hypothesis Significance Testing (NHST) Cheat Sheet
Null Hypothesis Significance Testing (NHST) is a statistical method used to determine if observed data provides enough evidence to reject a null hypothesis, which posits no effect or no difference. It's a cornerstone of scientific research for drawing conclusions from data.
Core Principles
- The null hypothesis (H0) assumes no effect or relationship, serving as a baseline to be challenged.
- The alternative hypothesis (H1 or Ha) proposes an effect or relationship exists.
- A p-value represents the probability of observing data as extreme as, or more extreme than, the actual observed data, assuming the null hypothesis is true.
- If the p-value is less than the chosen significance level (alpha, typically 0.05), the null hypothesis is rejected in favor of the alternative hypothesis.
- Type I error (alpha) is rejecting a true null hypothesis; Type II error (beta) is failing to reject a false null hypothesis.
Action Steps
- 1. Formulate the null (H0) and alternative (H1) hypotheses to define the research question.
- 2. Collect data relevant to the hypotheses.
- 3. Choose a significance level (alpha), commonly 0.05.
- 4. Calculate a test statistic based on the data and chosen statistical test.
- 5. Determine the p-value associated with the test statistic.
- 6. Compare the p-value to alpha: if p < alpha, reject H0; otherwise, fail to reject H0.
- 7. Interpret the results in the context of the research question.
Key Terms
- Null Hypothesis (H0): A statement that there is no significant difference or effect between groups or variables.
- Alternative Hypothesis (H1/Ha): A statement that there is a significant difference or effect, contradicting the null hypothesis.
- p-value: The probability of obtaining test results at least as extreme as the results actually observed, assuming that the null hypothesis is correct.
- Significance Level (alpha): The threshold (e.g., 0.05) used to decide whether to reject the null hypothesis; a p-value below alpha leads to rejection.
- Type I Error: Incorrectly rejecting a true null hypothesis (false positive).
- Type II Error: Incorrectly failing to reject a false null hypothesis (false negative).
Real World Examples
- A pharmaceutical company tests a new drug to see if it lowers blood pressure more than a placebo.: H0: The drug has no effect on blood pressure compared to placebo. H1: The drug lowers blood pressure. If p < 0.05, they reject H0 and conclude the drug is effective.
- A researcher investigates if a new teaching method improves student test scores compared to the traditional method.: H0: The new method has no effect on scores. H1: The new method improves scores. If p < 0.05, they reject H0 and conclude the new method is better.
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