Learn Then Test (LTT) is a threshold-selection framework based on multiple hypothesis testing proposed by Angelopoulos et al., whose core idea is to treat all candidate thresholds as a set of hypotheses to be tested, use finite-sample p-value construction methods to compute the risk upper bound corresponding to each threshold, and then use an FDR control procedure to select in one pass the set of thresholds that satisfy the risk budget.