Null hypothesis

Definition

A null hypothesis is a statement that there is no effect or no difference, and it serves as the default or starting assumption in hypothesis testing. It is typically denoted as $H_0$.

5 Must Know Facts For Your Next Test

1. The null hypothesis ($H_0$) often states that there is no significant difference between specified populations.
2. Rejection of the null hypothesis suggests that there is enough evidence to support the alternative hypothesis.
3. Failing to reject the null hypothesis means there isn't sufficient evidence to support the alternative hypothesis, but it does not prove that $H_0$ is true.
4. The p-value helps determine whether to reject the null hypothesis; a low p-value (typically < 0.05) indicates strong evidence against $H_0$.
5. In statistical tests, Type I error occurs when the null hypothesis is incorrectly rejected (false positive).

Review Questions

• What does the null hypothesis represent in a statistical test?
• How do you interpret a p-value when deciding whether to reject the null hypothesis?
• What are Type I and Type II errors in the context of the null hypothesis?

Related terms

Alternative Hypothesis: A statement indicating that there is an effect or a difference; it is denoted as $H_a$ or $H_1$.

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

Type I Error: An error that occurs when the null hypothesis is incorrectly rejected; also known as a false positive.