Rubin’s approach, called multiple imputation, takes that distribution into account. To use it, first make several copies of your data set. For a given missing value in one copy, randomly assign a guess from your distribution. By design, you’re more likely to pick one of the better guesses, but you’ll also have a small chance of picking one of the less plausible guesses. This process reflects the uncertainty in each guess. Repeat these steps for the missing value in each of the other copies of the data set.

Once you’ve filled in all the missing data, you can analyze each completed data set. You’ll end up with several different predictions for the effectiveness of your drug. Then you can use a recipe known as Rubin’s rules to pool your results and get an average prediction. By following these steps, you can also compute a better estimate of your final prediction’s uncertainty. For drug regulators like the U.S. Food and Drug Administration, being accurate about that uncertainty is crucial: It influences whether or not a drug will get approved.