1. Interval Estimation

2. Method Inductive Reasoning
Generalizing from a specific idea/notion/data-point
Start: a statistic is used as a “best point estimate” for a corresponding parameter, when it is computed using a random sample of data.
To generalize, we add and subtract a “margin for error” that creates a range of values for the parameter. This range is called the interval estimate
The width of the interval estimate is the product of 2 things:
The level of confidence, AND
The standard error of the statistic

3. Assumptions Data sample must be randomly selected
The size of the sample ought to be as large as possible, and should contain at least 30 observations—according to Central Limit Theorem.
If a sample is either not randomly selected or not large enough, then an interval estimate may fail to yield a valid generalization about a parameter’s value.