I'd welcome your thought on this situation: We have a dataset of patients with a rare condition (n=~10 for each of 2 groups). The participants completed a battery of standardized tests that use a mean of 100 and a standard deviation of 15. In some cases, participants did not pass the 'practice items' so did not start the task, and therefore there was no raw data to convert to a standard score. This resulted in 'missing' data throughout the dataset for different tests. We want to represent the performance of this participants in these cases. I was advised to enter the lowest standard score (e.g., 55) in these cases to be able to represent these cases and therefore have a full dataset. My questions are: (1) is this a version of Winsorizing if the missing data were replaced with a stand-in (such as standard scores of 55) recognizing that we do not change scores on the other end (since the rationale does not extend to that group)? (2) if this is statistically sound, are there citations or papers you could share so we could refer to them? (3) if this is statistically sound, would you recommend that we use the same score across standardized tests (e.g., 55), or look up the exact lowest possible standard score for each person based on their age and subtest?