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The image you shared refers to a statistical test result, where the test statistic is denoted by \( p \). It mentions that the result, referred to as the "p-value," shall be interpreted in some specific way.

In hypothesis testing, the p-value represents the probability that the observed data could occur under the null hypothesis. Typically, a lower p-value indicates stronger evidence against the null hypothesis. For example:

- If \( p \leq 0.05 \), the result is statistically significant, meaning the null hypothesis is likely to be rejected.
- If \( p > 0.05 \), the result is not statistically significant, meaning the null hypothesis may not be rejected.

Would you like more details on p-value interpretation or the hypothesis testing framework?

Here are 5 questions related to the concept:
1. What are Type I and Type II errors in hypothesis testing?
2. How is the significance level (alpha) related to the p-value?
3. What is the difference between a one-tailed and two-tailed test?
4. How do you calculate the p-value for different statistical tests?
5. What are common misconceptions about p-values in research?

**Tip**: Always report the p-value in context, and avoid interpreting it as the probability of the null hypothesis being true or false.

The result of a statistical test, denoted p, shall be interpreted as follows:

Learn how to interpret the p-value from a statistical test, including the threshold for statistical significance and how it informs decisions about the null hypothesis. This page covers the key concepts of hypothesis testing, p-value, and statistical significance, suitable for university-level statistics students.

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