In research methods in psychology, hypothesis testing is an important process used to determine whether the findings obtained from a study provide sufficient evidence to support or reject a research hypothesis. However, errors can occur during this process because decisions are made based on sample data rather than the entire population. These errors affect the accuracy and reliability of psychological research findings. The two major types of errors in hypothesis testing are Type I error and Type II error. A Type I error occurs when a researcher rejects the null hypothesis even though it is actually true. In other words, the researcher concludes that there is a significant relationship or effect when, in reality, no such effect exists. This is also known as a false positive error. For example, a psychologist may test a new therapy method and conclude that it is effective in reducing anxiety, although the therapy actually has no real impact. The researcher mistakenly identifies an effect due to chance variations in the sample data. The probability of committing a Type I error is represented by the significance level (alpha level) of a statistical test. Researchers commonly set the alpha level at 0.05, meaning there is a 5% chance of rejecting a true null hypothesis. In psychological research, reducing the alpha level can decrease the possibility of Type I error,