The p-value is a crucial concept in inferential statistics, primarily used to determine the statistical significance of results obtained from hypothesis testing. It represents the probability of obtaining test results at least as extreme as the observed results, assuming that the null hypothesis is true. In simpler terms, it helps researchers understand how likely it is that their findings are due to random chance rather than a true effect.

A smaller p-value indicates stronger evidence against the null hypothesis. For instance, a p-value of 0.05 suggests that there is a 5% chance that the observed results could occur under the null hypothesis. If the p-value is less than or equal to a pre-defined significance level (often set at 0.05), researchers typically reject the null hypothesis, concluding that the results are statistically significant ³⁴.

In practice, p-values are widely used in scientific research to assess the validity of hypotheses and the reliability of experimental results ².