What does a p-value indicate in the context of statistical significance?

A p-value is a fundamental concept in statistical hypothesis testing that helps determine the significance of results. More specifically, it quantifies the probability of observing the dataset, or something more extreme, if the null hypothesis is true. When researchers calculate a p-value, they are assessing how likely it is that the observed results occurred purely by chance, assuming there is no actual effect or relationship present in the population.

If the p-value is low (commonly below a threshold of 0.05), it indicates that such extreme results would be quite unlikely to occur just by random chance, leading researchers to reject the null hypothesis. Conversely, a high p-value suggests that the observed results could easily occur under the null hypothesis, indicating no statistically significant effect.

Understanding this concept is crucial because it directly informs researchers about the strength of the evidence against the null hypothesis, guiding them in their interpretation of their data and drawing conclusions.