The Type I error, otherwise known as the false positive rate, is an error of incorrectly rejecting a true null hypothesis. In other words, it is a type of statistical mistake in which you reject a null hypothesis that should have been accepted. This means that you are concluding that there was an effect when, in fact, there was none.

For example, a researcher may be trying to test whether or not watching violent television shows increases aggressive behavior in children. The researcher sets up the experiment correctly and collects data from children who watch violent television shows compared with those who do not. After analyzing the results, the researcher finds that those who watch violent television shows are more likely to act aggressively than those who did not watch them – indicating a possible causal relationship between exposure to violence and aggression. However, if this result is due to random chance or another factor outside of the scope of this study (such as some other form of entertainment), then it would be classified as a Type I Error – falsely claiming that there is an effect when there really isn’t one.