Understanding and interpreting p-values is crucial in a data science course. A p-value measures the probability of observing results as extreme as the sample data if the null hypothesis is true. Lower p-values indicate stronger evidence against the null hypothesis. Interpreting p-values helps data scientists assess statistical significance, avoid false conclusions, and make reliable, data-driven decisions in analytical workflows.
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