Differentiate type 1 error and type 2 error in research?

Type 1 error and type 2 error are two types of errors that can occur in research studies, particularly in hypothesis testing.

1. Type 1 error: Also known as a false positive, a type 1 error occurs when the researcher rejects a null hypothesis that is actually true. In other words, it is the incorrect rejection of a true null hypothesis. This error is typically caused by statistical significance testing and can lead to the conclusion that there is a significant effect or relationship when there is none. The probability of committing a type 1 error is denoted by the symbol α (alpha) and is typically set at a predetermined level (e.g., 0.05 or 0.01) to control the risk of making this error.

2. Type 2 error: Also known as a false negative, a type 2 error occurs when the researcher fails to reject a null hypothesis that is actually false. In other words, it is the incorrect acceptance of a false null hypothesis. This error is typically caused by a lack of statistical power, which refers to the ability of a study to detect a true effect or relationship. A type 2 error can lead to the conclusion that there is no significant effect or relationship when there actually is one. The probability of committing a type 2 error is denoted by the symbol β (beta) and is influenced by factors such as sample size, effect size, and the chosen level of significance (α).

In summary, a type 1 error involves rejecting a true null hypothesis, while a type 2 error involves failing to reject a false null hypothesis. Both types of errors can have implications for the validity and reliability of research findings.