We evaluate two diagnostic tools used to determine if counterfactual analysis requires extrapolation. Counterfactuals based on extrapolation are model dependent and might not support empirically valid inferences. The diagnostics help researchers identify those counterfactual “what if” questions that are empirically plausible. We show, through simple Monte Carlo experiments, that these diagnostics will often detect extrapolation, suggesting that there is a risk of biased counterfactual inference when there is no such risk of extrapolation bias in the data. This is because the diagnostics are affected by what we call the n/k problem: as the number of data points relative to the number of explanatory variables decreases, the diagnostics are more likely to detect the risk of extrapolation bias even when such risk does not exist. We conclude that the diagnostics provide too severe a test for many data sets used in political science.