A common criticism of null hypothesis testing stems from researchers misinterpreting the $p$-value. A frequent error is believing that the $p$-value represents the probability that the null hypothesis is true, rather than the probability of observing the sample data if the null hypothesis were true. Another closely related misconception is assuming that $1 - p$ equals the probability of replicating a statistically significant result, a claim that is overly optimistic and ignores the crucial role of statistical power in replication.