What is the null hypothesis in statistical testing?

What is the null hypothesis in statistical testing?

• A. The hypothesis that there is no significant difference between specified populations, any observed difference being due to sampling or experimental error.
• B. The hypothesis that there is a significant difference between populations.
• C. The hypothesis that the population parameter equals a specified value.
• D. The hypothesis that the data are normally distributed.

Answer: (A)

In hypothesis testing, the null hypothesis is the statement that there is no real difference or effect between the populations being compared; any observed difference is due to sampling variation or experimental error. This creates a baseline that the data are used to challenge. When results show enough evidence against this baseline, you can reject it in favor of the alternative hypothesis, which asserts that a real difference or effect exists. This definition fits the common aim of H0: to express "no difference" between populations, so that statistical tests can quantify whether the data contradict that claim. The option that says there is a significant difference describes the alternative hypothesis, not the null. The option about the population parameter equaling a specified value can be a concrete form of a null in some tests, but the overarching idea is still about no real difference or effect. The statement about data being normally distributed is a methodological assumption, not the hypothesis about populations.

In hypothesis testing, the null hypothesis is the statement that there is no real difference or effect between the populations being compared; any observed difference is due to sampling variation or experimental error. This creates a baseline that the data are used to challenge. When results show enough evidence against this baseline, you can reject it in favor of the alternative hypothesis, which asserts that a real difference or effect exists.

This definition fits the common aim of H0: to express "no difference" between populations, so that statistical tests can quantify whether the data contradict that claim. The option that says there is a significant difference describes the alternative hypothesis, not the null. The option about the population parameter equaling a specified value can be a concrete form of a null in some tests, but the overarching idea is still about no real difference or effect. The statement about data being normally distributed is a methodological assumption, not the hypothesis about populations.