What does a p-value signify?

A p-value is a fundamental concept in statistical hypothesis testing that quantifies the probability of observing the data, or something more extreme, assuming that the null hypothesis is true. This means that the p-value represents the likelihood of obtaining the observed results due to random chance alone if the null hypothesis holds.

For instance, if a p-value is calculated to be 0.05, this implies that there is a 5% chance of observing such an extreme outcome under the assumption that the null hypothesis is true. Therefore, the lower the p-value, the less likely the observed data would occur by random chance, which provides evidence against the null hypothesis.

While the other options might seem relevant to statistical analysis, they do not accurately capture the essence of what a p-value signifies in hypothesis testing. The p-value does not reflect the probability of the hypothesis itself being true, nor does it measure the variation in the data or the strength of relationships between variables. Understanding the correct interpretation of p-values is critical for conducting and interpreting statistical tests effectively.