To calculate a p‑value from a 2×2 contingency table, you first tabulate your observed counts—say “exposed” versus “unexposed” on one axis and “event” versus “no event” on the other—and then apply Fisher’s exact test (or chi‑square when cell sizes permit) to compute the probability of observing a table as extreme as (or more extreme than) yours under the null hypothesis of no association. 

The resulting p‑value, expressed to four decimal places, tells you how likely it is that any difference between groups arose by chance alone. In an MD, MS, DNB or superspeciality thesis—where you might compare complication rates in two surgical techniques, prevalence of a laboratory marker across patient cohorts, or response rates to different treatment protocols—this simple test allows you to rigorously assess whether your categorical findings are statistically meaningful. 

A p‑value below the conventional 0.05 threshold suggests a statistically significant association worthy of further clinical consideration, while a larger p‑value cautions against over‑interpreting random variation as real effect. 

Always check that your sample size and cell counts justify the test chosen, complement your p‑value with confidence intervals or effect‑size measures, and discuss any borderline or unexpected results in light of study limitations.