Popular DS interview question: What are confidence and prediction intervals?

Statistical estimates always have some uncertainty.

Consider a simple example of modeling house prices just based on its area using linear regression.

A prediction wouldn’t tell the true value of a house based on its area. This is because different houses of the same size can have different prices.

Instead, what it predicts is the mean value related to the outcome at a particular input.

The point is…

There’s always some uncertainty involved in statistical estimates, and it is important to communicate it.

In this specific case, there are two types of uncertainties:
1) The uncertainty in estimating the true mean value.
2) The uncertainty in estimating the true value.

Confidence interval and prediction interval help us capture these uncertainties.

I have often seen people getting confused between the two, so I covered it in detail here: https://lnkd.in/gcjD_qaH.

Communicating these uncertainties is quite crucial in decision-making because it provides a clearer understanding of the reliability and precision of predictions.

This transparency allows stakeholders to make more informed decisions by considering the range of possible outcomes and the associated risks.