The article debunks common misconceptions in statistics. It clarifies that statistical significance doesn't always mean importance, rejecting the null hypothesis doesn't automatically prove the alternative, and p-value isn't the probability of the null hypothesis being correct. It also emphasizes the importance of confidence intervals over null hypothesis tests and highlights that power can't justify accepting the null hypothesis. Lastly, it points out that failing to reject the null hypothesis when p is greater than α isn't always correct.