Introduction to Statistics for Data Science

Advanced Level — The Fundamentals of Inferential Statistics with Point Estimators and Confidence Intervals Estimates

In Statistics, to infer the value of an unknown parameter we use estimators. Estimation is the process used to make inferences, from a sample, about an unknown population parameter.

Based on a random sample of a population, a point estimate is the best estimate although it is not absolutely accurate. Furthermore, if you continuously retrieve random samples from the same population it is expected that the point estimate would vary from sample to sample.

On the other hand, a confidence interval is an estimate constructed on assumption that the true parameter will fall within a specified proportion regardeless of the number of samples analysed.

A population estimator is an approximation depending solely on sample information, while on the other hand, a specific value is called an estimate.

As we’ve referred, there are two types of estimates:

• Point Estimates — single number.
• Confidence Interval Estimates — provide much more information, and are preferred when making inferences.