What are the two main types of errors in hypothesis testing?

In hypothesis testing, the two main types of errors are Type I error and Type II error. A Type I error occurs when the null hypothesis is rejected when it is actually true. This means that the researcher concludes that there is an effect or difference when, in fact, there is none. This type of error is often denoted by alpha (α), which represents the probability of making this mistake.

On the other hand, a Type II error happens when the null hypothesis is not rejected when it is false. In this case, the researcher fails to detect an effect or difference that actually exists. This error is denoted by beta (β) and reflects the probability of missing a true finding.

Understanding these two errors is crucial for interpreting the results of statistical tests. Researchers aim to minimize both types of errors to enhance the validity of their conclusions, balancing the risk of false positives and false negatives when designing and conducting studies.