Correct and Incorrect Conclusions in NHST.

Note. In Null Hypothesis Significance Testing (NHST), the null hypothesis of no effect or no difference is either true (cells A and B) or false (cells C and D). When the null hypothesis is true (i.e., the left-hand column), it is possible for a researcher to make an incorrect decision by obtaining a significant result and rejecting the null hypothesis (cell B). The probability of this happening is equal to α and is set to 5%, by convention, to help minimize the false positive. When the null hypothesis is false (i.e., the right-hand column), the researcher can make a correct decision by obtaining a significant result (cell D). The probability of this happening is (1 – β), or the statistical power of the test. When the null hypothesis is false, one can make an inferential error by failing to obtain a significant result (cell C). This error rate is defined as beta (β) and is commonly referred to as Type II error. Correct and Incorrect Conclusions in NHST.