Prediction interval
![Prediction interval (on the y-axis) given from z (the quantile of the standard score, on the x-axis). The y-axis is logarithmically compressed (but the values on it are not modified).](/Images/godic/202502/04/Standard_score_and_prediction_interval.svg4834.png")
![Diagram showing the cumulative distribution function for the normal distribution with mean (µ) 0 and variance (σ2) 1. In addition to the quantile function, the prediction interval for any standard score can be calculated by (1 − (1 − Φµ,σ2(standard score))·2). For example, a standard score of x = 1.96 gives Φµ,σ2(1.96) = 0.9750 corresponding to a prediction interval of (1 − (1 − 0.9750)·2) = 0.9500 = 95%.](/Images/godic/202502/04/Cumulative_distribution_function_for_normal_distribution,_mean_0_and_sd_1.svg4834.png")
In statistical inference, specifically predictive inference, a prediction interval is an estimate of an interval in which future observations will fall, with a certain probability, given what has already been observed. Prediction intervals are often used in regression analysis.