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The question is asking which of the provided statements best defines "statistical significance."

The most accurate definition of statistical significance is usually connected to a probability threshold (often set at 0.05 or 5%) that helps us determine whether the observed results are likely due to chance or not. The statement that best captures this idea is:

**"There is less than 5% probability of obtaining the observed results if the null hypothesis is true."**

This statement corresponds to the idea that if a test yields a p-value less than 0.05 (for a typical significance level), it indicates that the observed data is unlikely to have occurred purely by random chance under the null hypothesis.

Would you like further clarification or additional details on this concept?

Here are five follow-up questions related to statistical significance:
1. What is a null hypothesis in hypothesis testing?
2. How is the p-value used in determining statistical significance?
3. What does a p-value of 0.01 indicate in comparison to 0.05?
4. How does increasing sample size affect the statistical significance of results?
5. What are Type I and Type II errors in the context of hypothesis testing?

**Tip**: Remember, statistical significance does not always imply practical significance—an important distinction when interpreting results!

Which of the following statements best defines statistical significance?

Learn about statistical significance and its role in hypothesis testing. This guide explains how the p-value determines the likelihood of observing results due to chance, focusing on the threshold of 5%. Suitable for college-level or advanced high school students.

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