Explain what a type I and type II error is

A type I error, also known as a false positive, occurs when we reject a null hypothesis that is actually true. In other words, it is an incorrect rejection of the null hypothesis. This means that we conclude there is a significant effect or relationship when, in reality, there is none. The probability of making a type I error is denoted by the Greek letter alpha (α) and is called the significance level. A lower significance level means a lower probability of making a type I error.

A type II error, also known as a false negative, occurs when we fail to reject a null hypothesis that is actually false. In other words, it is an incorrect acceptance of the null hypothesis. This means that we conclude there is no significant effect or relationship when, in reality, there is one. The probability of making a type II error is denoted by the Greek letter beta (β). The power of a statistical test is equal to 1 - β and represents the probability of correctly rejecting the null hypothesis when it is false.

Both type I and type II errors are important concepts in hypothesis testing and statistical analysis. Researchers aim to minimize both types of errors by carefully choosing the significance level and sample size, as well as conducting power analyses to ensure sufficient statistical power