Researchers should be aware that statistical significance tests carry the risk of getting it wrong. One of two types of basic errors could occur. A Type 1 error is known as a false positive result—the test finds statistical significance when, in fact, there isn’t any. In this case, the researcher should have accepted the null hypothesis. A Type 2 error is a false negative—the test finds no statistically significant relationship between the variables, when there actually is one.