The p-value in hypothesis testing represents is the probability of observing results as extreme or more extreme than currently observed, given that the null hypothesis is true correct option (d).
The p-value is a statistical measure that represents the probability of obtaining results as extreme or more extreme than the ones observed, assuming the null hypothesis is true. In other words, it's the likelihood of observing the current results or more extreme ones, even if the null hypothesis is actually true.
The null hypothesis (H₀) typically states that there is no significant difference or effect between the groups being compared. The p-value helps us determine whether to reject or fail to reject the null hypothesis based on the observed data.
A small p-value (usually less than or equal to 0.05) indicates that the observed results are unlikely to have occurred by chance, suggesting that the null hypothesis should be rejected and an alternative hypothesis (H₁) accepted. This means there's a low probability of observing these results if the null hypothesis were true.