 Wouldn't it be easier if there was a way to remember the difference between type one and type two errors, so you never had to look it up again. This is the story of the boy who cried type one error. You might be familiar with the story of the boy who cried wolf, the villagers in that story make both types of error in order. In this example, the villagers null hypothesis is that there is no wolf. Their alternative hypothesis is that there is a wolf. Like many good researchers, they collect some data, test their null hypothesis and infer conclusions. Based on the results of their study, they will either fail to reject the null hypothesis and continue to relax, or they will reject the null hypothesis and rush to help any neighbor who is endangered by wolves. In the first part of the story, a boy travels into the woods and decides to play a trick on the villagers. Despite the fact there are no wolves to be seen and he is perfectly safe, he screams for help. And so despite the null hypothesis being true, the villagers observed the boy's screams and rushed to help. They made their first error a type one error in which they had falsely rejected a true null hypothesis, also known as a false positive. In the second part of the story, the boy travels into the woods again, but this time there is a wolf. Seeing the wolf, he again screams for help. This time, despite the null hypothesis being false, the villagers fail to reject it and so they stay at home with their feet up. They made their second error a type two error in which they had failed to reject a false null hypothesis, also known as a false negative. In our studies, we can calculate the number of observations required to achieve the desired type one and type two error rates, given the expected or smallest meaningful effect size. To calculate this in G-Power, check out this five minute tutorial. And now that you know the difference between the errors, try to avoid these 10 common statistical mistakes.