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Poorly formatted data can impact various facets of predictive modeling, making the option that encompasses all potential effects the most accurate choice.

When data is poorly formatted, it can lead to unclear insights and conclusions because the inconsistencies or errors may obscure valuable patterns or relationships that the model should leverage for predictions. This lack of clarity can arise due to misaligned data types, missing values, or incorrect categorizations.

Moreover, poorly formatted data often skews the results of statistical tests. In predictive modeling, statistical assumptions such as normality and homoscedasticity rely on well-structured data. Violations of these assumptions can lead to biased estimates and invalid results, severely impacting the model's predictive power.

Lastly, data that is not formatted correctly complicates sorting and analyzing, making it challenging to derive meaningful information. For instance, if dates are in different formats or categorical variables are not consistently labeled, the process of preparation for modeling becomes arduous, increasing the likelihood of errors and further diminishing the data's utility.

Overall, recognizing that poorly formatted data undermines the entire data processing and modeling pipeline underpins the rationale for choosing the option that includes all negative influences of poor formatting.