👉 Confidence math in statistics quantifies the reliability of a model's predictions by measuring how certain the model is about its outcomes. It's typically represented by a value between 0 and 1, where 1 indicates absolute certainty or perfect confidence in the prediction, and 0 signifies complete uncertainty. For instance, a model predicting a coin flip result might have a confidence interval of 0.95, meaning the model is 95% confident that the actual outcome will fall within a certain range (e.g., 0.90 to 1.05). This metric is crucial for decision-making, as it helps assess the risk associated with predictions and guides when to trust a model's output.
