1. The problem is to find the critical value or p-value for a given statistical test. 2. The critical value is a point on the test distribution that defines the boundary for rejecting the null hypothesis. 3. The p-value is the probability of obtaining a test statistic at least as extreme as the one observed, assuming the null hypothesis is true. 4. To find the critical value, use the significance level $\alpha$ and the distribution type (e.g., normal, t-distribution). 5. For example, for a two-tailed test with $\alpha=0.05$ and a normal distribution, the critical values are $\pm z_{\alpha/2} = \pm 1.96$. 6. To find the p-value, calculate the test statistic and then find the probability of observing a value as extreme or more extreme using the distribution's cumulative distribution function (CDF). 7. If the p-value is less than $\alpha$, reject the null hypothesis; otherwise, do not reject it. 8. In brief, critical value is a cutoff point from the distribution, and p-value is the probability measure to decide on the null hypothesis.