This paper takes a critical look at a common but potentially misleading practice in statistics: setting up one-sided null hypotheses with just an equal sign. We uncover major problems with this method, specifically leading to what we call Type III and Type IV errors. By examining real-world examples, like testing average IQs and evaluating medical treatments that claim incredibly high success rates, we show how this approach can lead to strange and illogical results. Finally, we argue for a change in how we create these hypotheses. We suggest that null hypotheses should cover all possible outcomes that the alternative hypothesis doesn’t address. This change could lead to more reliable and accurate results in statistical studies.