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Bias in the data can significantly skew experimental results by leading to incorrect conclusions or interpretations. For instance, if the data collection process favors certain outcomes or populations, it may not accurately represent the broader context or reality, resulting in misleading findings. This bias can undermine the validity and reliability of the study, ultimately affecting its applicability and the trustworthiness of its conclusions. Addressing and minimizing bias is crucial for ensuring that experimental results are both accurate and generalizable.

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