Data preprocessing is a crucial step in the data analysis pipeline where raw data is cleaned and transformed to prepare it for analysis or modeling. While preprocessing can help in dealing with missing, inconsistent, or noisy data, it can also introduce biases if not done carefully. Here are some examples of data preprocessing bias, some of which can be illustrated using the credit union context:

1. Imputation of Missing Data: If a credit union imputes missing data on loan applications—say, filling in missing income values with the average income—it might introduce bias if the missingness isn't random. For example, if higher-income individuals are more likely to leave that field blank, the imputation could underestimate their income.

2. Feature Scaling: Standardizing or normalizing features (like income or loan amounts) can inadvertently give more weight to certain features over others in some algorithms, affecting the outcome of analyses or predictions.

3. Data Smoothing: While smoothing can help reduce noise in data, over-smoothing might eliminate genuine fluctuations or trends. For instance, smoothing out fluctuations in monthly deposits might miss genuine patterns, like seasonal effects.

4. Binning Continuous Variables: Converting a continuous variable, like age, into bins (e.g., 18-25, 26-35) can lead to loss of information and might introduce arbitrary boundaries. Two members aged 25 and 26 would be placed in separate bins, even though they're close in age.

5. Oversampling and Undersampling: To address class imbalance, like in a dataset where loan defaults are rare, one might oversample the default cases or undersample the non-default cases. While this can help models perform better, it can also introduce bias and affect the generalizability of the model.

6. Removing Outliers: If a credit union decides to remove all loan applications that request unusually high amounts, considering them outliers, it might inadvertently exclude genuine cases or specific segments of the population.

7. Feature Selection: Choosing which variables to include in a model based on some criteria might leave out important variables. If a credit union uses only employment status and income to predict loan default and ignores credit history, the model might be biased.

8. One-Hot Encoding: When categorical variables are converted into binary columns, the increase in dimensionality can affect some models. If not handled correctly, this can lead to multicollinearity or overfitting.

9. Temporal Splitting: In time-series data, splitting data randomly for training and testing can lead to future information leaking into the past. For credit unions, this could mean using future financial data to predict past events, which is not realistic.

10. Ignoring Data Dependencies: If a credit union has multiple accounts for a single member and treats each account as an independent data point, it might ignore the inherent correlations between a member's accounts, leading to biased models.

To mitigate data preprocessing biases, it's essential to understand the data, the context, and the implications of preprocessing decisions. Validating models or analyses on diverse datasets and continually re-evaluating preprocessing choices are also good practices.