Type I (Alpha) error: A Type I error, also known as a false positive, occurs when a null hypothesis is rejected when it is actually true. In other words, th...
Type I (Alpha) error:
A Type I error, also known as a false positive, occurs when a null hypothesis is rejected when it is actually true. In other words, the researcher incorrectly concludes that the null hypothesis is false when it is actually true.
Type II error:
A Type II error, also known as a false negative, occurs when the null hypothesis is not rejected when it is actually false. In other words, the researcher incorrectly concludes that the null hypothesis is true when it is actually false.
The probability of making a Type I error, also known as the false alarm rate, is controlled by the significance level (α). The significance level is the maximum p-value that the researcher is willing to accept. If the p-value is less than α, the researcher rejects the null hypothesis.
The probability of making a Type II error, also known as the false rejection rate, is controlled by the power of the test. The power of a test is the probability of correctly rejecting the null hypothesis when it is false.
In conclusion, Type I and Type II errors are two important measures of the risk associated with statistical tests. The decision to reject or fail to reject the null hypothesis depends on the level of significance, which is determined by the researcher