Are female hurricanes more deadly? In this chapter we demonstrate multiverse analysis using analytical inputs from many scholars in a high-profile empirical debate. In results from more than 10,000 model specifications, only 12 percent of estimates are statistically significant and 99 percent are smaller in magnitude than what the original authors reported. Multiverse analysis shows that some published findings are extremely weak and nonrobust.

Why do different models give different results? Which modeling assumptions matter most? These are questions of model influence. Standard regression results fail to address simple questions like, which control variables are important for getting this result? In this chapter we lay out a framework for thinking about influence and draw on empirical examples to illustrate. When a result is not fully robust, the influence analysis provides methodological explanations for the failure of robustness. These explanations can be considered methodological scope conditions – they explain why a hypothesis can be supported in some cases but not in others. We also show how multiverse results can help inform the method of sensitivity analysis