Given the prevalence of missing data on species' traits - the Raunkiaeran shortfall-, several methods have been proposed to fill sparse databases. However, analyses based on these imputed databases can introduce several biases. Here, we evaluated potential estimation biases caused by the use of imputed databases. In the evaluation, we considered the estimation of descriptive statistics, regression coefficient, and phylogenetic signal for different missing and imputing scenarios. We found that percentage of missing data, missing mechanisms and imputation methods were important in determining estimation errors. Imputation errors are not linearly related to estimate errors. Adding phylogenetic information provides better estimates of the evaluated statistics, but this information should be combined with other variables such as traits correlated to the missing data variable. Using an empirical dataset, we found that even traits that are strongly correlated to each other, such as brain and body size of primates, can produce biases when estimating phylogenetic signal from missing data datasets. We advise researchers to share both their raw and imputed data as well as to consider the pattern of missing data to evaluate methods that perform better for their goals. In addition, the performance of imputation methods should be mainly based on statistical estimates instead of only in imputation error.
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