Hypothesis test

Definition

A hypothesis test is a statistical method used to make inferences about a population parameter based on a sample statistic. It involves formulating a null hypothesis and an alternative hypothesis, then using sample data to decide whether to reject the null hypothesis.

5 Must Know Facts For Your Next Test

1. The null hypothesis ($H_0$) represents no effect or no difference, and it is the assumption that is initially presumed true.
2. The alternative hypothesis ($H_A$ or $H_1$) represents an effect or difference and is what you aim to support with evidence from your sample.
3. The significance level (alpha, $\alpha$) is the threshold for determining whether your test result is statistically significant, commonly set at 0.05.
4. A p-value indicates the probability of observing the sample data, or something more extreme, if the null hypothesis is true; a p-value less than $\alpha$ leads to rejection of $H_0$.
5. Commonly used probability distributions for hypothesis testing include the normal distribution (for large samples) and the t-distribution (for small samples).

Review Questions

• What are the roles of the null hypothesis ($H_0$) and alternative hypothesis ($H_A$)?
• How does the significance level ($\alpha$) influence decision-making in a hypothesis test?
• What does a p-value represent in the context of a hypothesis test?

Related terms

Null Hypothesis: $H_0$: A statement that there is no effect or no difference, serving as the default assumption.

Alternative Hypothesis: $H_A$: A statement that indicates the presence of an effect or difference, opposing $H_0$.

p-value: The probability of obtaining test results at least as extreme as those observed during the test, assuming that $H_0$ is true.