Normalization techniques are methods used in data processing to adjust values in a dataset to a common scale. This helps to eliminate biases caused by different units or ranges, making it easier to compare and analyze data. Common techniques include min-max normalization, which rescales values to a range between 0 and 1, and z-score normalization, which standardizes values based on their mean and standard deviation. These techniques are particularly useful in fields like machine learning and statistics, where consistent data representation is crucial for model performance. By applying normalization, analysts can improve the accuracy of their results and ensure that all features contribute equally to the analysis.