Dichotomous thinking
In statistics, dichotomous thinking or binary thinking is the process of seeing a discontinuity in the possible values that a p-value can take during null hypothesis significance testing: it is either above the significance threshold or below. When applying dichotomous thinking, a first p-value of 0.0499 will be interpreted the same as a p-value of 0.0001 while a second p-value of 0.0501 will be interpreted the same as a p-value of 0.7. The fact that first and second p-values are mathematically very close is thus completely disregarded and values of p are not considered as continuous but are interpreted dichotomously with respect to the significance threshold. A common measure of dichotomous thinking is the cliff effect.
Dichotomous thinking is very often associated with p-value reading but it can also happen with other statistical tools such as interval estimates.
