In hypothesis testing, what does the null hypothesis typically represent?

In hypothesis testing, what does the null hypothesis typically represent?

• A. An assumption of no effect or status quo.
• B. An assertion that the data are randomly distributed.
• C. An assertion that the alternative is true.
• D. A statement about the sampling method.

Answer: (A)

Null hypothesis represents an assumption of no effect or the status quo. It sets a baseline: if there’s truly no difference, no change, or no association, the data should look a certain way under this assumption. In hypothesis testing you start by assuming this no-effect statement is true, then check whether the observed data are improbable under that assumption. If the results are unlikely enough (below your chosen significance level), you have evidence against the null and in favor of the alternative, which is the claim there is an effect or difference. The other options don’t fit because the null isn’t a claim that the data are randomly distributed, it isn’t asserting that the alternative is true, and it isn’t about how the sampling is conducted.

Null hypothesis represents an assumption of no effect or the status quo. It sets a baseline: if there’s truly no difference, no change, or no association, the data should look a certain way under this assumption. In hypothesis testing you start by assuming this no-effect statement is true, then check whether the observed data are improbable under that assumption. If the results are unlikely enough (below your chosen significance level), you have evidence against the null and in favor of the alternative, which is the claim there is an effect or difference. The other options don’t fit because the null isn’t a claim that the data are randomly distributed, it isn’t asserting that the alternative is true, and it isn’t about how the sampling is conducted.