The primary concern with sparse data is that it can lead to statistical power issues. Low statistical power reduces the likelihood of detecting true associations, which can result in either Type I errors (false positives) or Type II errors (false negatives). This makes it difficult to identify risk factors, measure associations, and evaluate interventions accurately.
